{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"rivet","slug":"rivet","name":"Rivet","type":"framework","url":"https://github.com/Ironclad/rivet","page_url":"https://unfragile.ai/rivet","categories":["app-builders","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"rivet__cap_0","uri":"capability://planning.reasoning.node.based.visual.graph.editor.for.ai.workflow.design","name":"node-based visual graph editor for ai workflow design","description":"Provides a Tauri-based desktop application with a visual node-and-edge graph editor for designing LLM workflows without code. The editor implements a graph data model where nodes represent computational units (LLM calls, data transforms, control flow) and edges represent data flow between them. Users drag nodes onto a canvas, configure node parameters through UI panels, and connect outputs to inputs. The graph is serialized to JSON for persistence and execution.","intents":["Design complex multi-step LLM prompts visually without writing code","Build conditional logic and loops in AI workflows using node connections","Test and iterate on prompt chains interactively before production deployment","Share workflow designs with non-technical stakeholders through visual representation"],"best_for":["AI product teams building LLM applications without dedicated ML engineers","Prompt engineers iterating on complex multi-step reasoning chains","Organizations wanting visual audit trails of AI decision logic"],"limitations":["Desktop-only application (Tauri) — no web-based collaborative editing","Graph complexity scales visually; very large graphs (100+ nodes) become difficult to navigate","No built-in version control — requires manual export/import for collaboration"],"requires":["Tauri runtime (Windows, macOS, Linux)","At least 4GB RAM for smooth graph rendering with 50+ nodes","API keys for LLM providers (OpenAI, Anthropic, etc.)"],"input_types":["node configuration (text, numbers, boolean flags)","graph structure (JSON serialization)","runtime data (strings, objects, arrays)"],"output_types":["visual graph representation","serialized graph JSON","execution results displayed in node output panels"],"categories":["planning-reasoning","visual-programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_1","uri":"capability://automation.workflow.graph.processor.with.local.and.remote.execution.modes","name":"graph processor with local and remote execution modes","description":"Executes serialized graph definitions through a graph processor engine that traverses nodes in dependency order, manages execution state, and handles both local (in-process) and remote (debugger-connected) execution. The processor implements a process context that tracks variable bindings, execution history, and node outputs. Local execution runs graphs directly in Node.js or browser; remote execution connects to a debugger for step-through debugging and inspection.","intents":["Run AI workflows locally without cloud infrastructure for testing and development","Debug graph execution step-by-step with variable inspection at each node","Execute the same graph definition in production via embedded Node.js library","Record execution traces for auditing and replay of LLM interactions"],"best_for":["Teams building self-hosted AI applications with @ironclad/rivet-node","Developers debugging complex multi-step LLM chains interactively","Organizations requiring execution audit trails for compliance"],"limitations":["Local execution blocks on LLM API calls — no built-in async batching or request pooling","Process context stored in memory — no persistence across restarts without explicit serialization","Remote debugging requires active debugger connection; no async replay from logs"],"requires":["Node.js 18+ for @ironclad/rivet-node execution","Graph definition in Rivet JSON format","API credentials for any LLM nodes in the graph"],"input_types":["graph JSON definition","process context (variable bindings)","node input values (strings, objects, arrays)"],"output_types":["execution results (node outputs)","process context with all variable bindings","execution recording (trace of all node executions)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_10","uri":"capability://text.generation.language.prompt.designer.and.template.system","name":"prompt designer and template system","description":"Integrated prompt design tool for crafting and testing LLM prompts before using them in graphs. The prompt designer provides a text editor with syntax highlighting, variable interpolation (using {{variable}} syntax), and a preview pane showing how prompts render with sample data. Designed prompts can be exported as graph nodes.","intents":["Design and iterate on prompts with live preview before committing to workflows","Test prompts with sample data to validate output quality","Create reusable prompt templates with variable placeholders","Manage prompt versions and variations for A/B testing"],"best_for":["Prompt engineers optimizing LLM outputs before production use","Teams running A/B tests on different prompt variations","Organizations documenting prompt engineering decisions"],"limitations":["Prompt designer is separate from graph execution — requires manual copy/paste to integrate","No built-in prompt optimization or quality scoring — relies on manual evaluation","Variable interpolation limited to simple string substitution — no complex templating"],"requires":["Prompt text (string)","Sample data for variable interpolation (objects or arrays)","LLM API credentials for testing"],"input_types":["prompt template (string with {{variable}} placeholders)","sample data (objects or arrays)","LLM configuration (model, temperature, etc.)"],"output_types":["rendered prompt (string)","LLM response (string)","prompt node definition (JSON)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_11","uri":"capability://automation.workflow.cli.and.server.mode.for.production.deployment","name":"cli and server mode for production deployment","description":"Command-line interface and server mode enabling Rivet graphs to run in production environments without the desktop IDE. The CLI can execute graphs directly from the command line, passing inputs via arguments or stdin. Server mode runs Rivet as an HTTP server exposing graphs as REST API endpoints, enabling integration with existing applications.","intents":["Deploy Rivet graphs to production servers without the desktop application","Expose graphs as REST APIs for integration with web applications","Run graphs in CI/CD pipelines or scheduled jobs","Execute graphs in containerized environments (Docker, Kubernetes)"],"best_for":["Teams deploying AI workflows to production infrastructure","Organizations integrating Rivet graphs into existing REST API architectures","DevOps teams automating workflow execution in CI/CD pipelines"],"limitations":["Server mode requires manual API endpoint configuration — no auto-generated OpenAPI specs","No built-in authentication or authorization — requires external API gateway","CLI execution is synchronous — no built-in async job queuing or scheduling"],"requires":["Node.js 18+ runtime","Graph definition file (JSON)","API credentials for LLM providers","Docker (optional, for containerized deployment)"],"input_types":["graph file path (string)","input data (JSON via CLI args or HTTP request body)","environment variables (for API credentials)"],"output_types":["execution results (JSON via stdout or HTTP response)","exit code (0 for success, non-zero for failure)","error messages (stderr or HTTP error response)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_12","uri":"capability://automation.workflow.remote.debugging.with.debugger.connection","name":"remote debugging with debugger connection","description":"Enables step-through debugging of graph execution by connecting the desktop IDE to a running graph execution (local or remote). The debugger allows pausing execution at nodes, inspecting variable values, stepping through execution, and modifying execution state. Debugger connection is established via WebSocket or HTTP, allowing debugging of graphs running on remote servers.","intents":["Debug graph execution step-by-step to understand why outputs are incorrect","Inspect variable values at specific points in execution","Debug graphs running on remote servers without local reproduction","Understand execution flow through complex conditional and loop logic"],"best_for":["Developers debugging complex multi-step workflows","Teams troubleshooting production issues in staging environments","Prompt engineers understanding why LLM outputs are unexpected"],"limitations":["Debugger connection requires active IDE session — cannot debug unattended executions","Stepping through LLM calls requires waiting for API responses — slow for large models","Debugger state is not persisted — session lost if connection drops"],"requires":["Desktop IDE running with debugger enabled","Graph execution with debugger connection configured","Network connectivity between IDE and execution environment"],"input_types":["debugger connection details (host, port)","breakpoint definitions (node IDs)"],"output_types":["execution state (variable values, current node)","execution trace (nodes executed so far)","error messages (if execution fails)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_13","uri":"capability://tool.use.integration.embedded.execution.via.ironclad.rivet.node.npm.package","name":"embedded execution via @ironclad/rivet-node npm package","description":"Provides a Node.js-specific package (@ironclad/rivet-node) for embedding Rivet graph execution directly into Node.js applications. Applications import the package, load a graph definition, and execute it programmatically with input data. The package provides APIs for graph loading, execution, and result retrieval, enabling Rivet graphs to be used as a library within larger applications.","intents":["Embed Rivet graphs into existing Node.js applications without separate deployment","Use Rivet graphs as reusable components in larger AI systems","Execute graphs programmatically with dynamic inputs from application logic","Integrate Rivet with existing Node.js frameworks (Express, Next.js, etc.)"],"best_for":["Node.js developers integrating Rivet graphs into existing applications","Teams building AI features within larger products","Organizations wanting to use Rivet as a library rather than a separate service"],"limitations":["Node.js only — no Python, Go, or other language support","Graph execution runs in the same process — no isolation or resource limits","No built-in caching or memoization — repeated executions re-run all nodes"],"requires":["Node.js 18+ runtime","@ironclad/rivet-node npm package installed","Graph definition file (JSON)","API credentials for LLM providers"],"input_types":["graph definition (JSON or loaded from file)","input data (objects or primitives)","execution options (timeout, context, etc.)"],"output_types":["execution results (any type)","execution metadata (timing, token usage, etc.)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_2","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"Abstracts LLM interactions through a provider-agnostic interface supporting OpenAI, Anthropic, and other models. Chat nodes in the graph accept a model identifier and configuration (temperature, max tokens, system prompt) and route calls to the appropriate provider's API. The abstraction handles provider-specific differences in API contracts, token counting, and response formats, normalizing them to a common interface.","intents":["Switch between LLM providers (OpenAI GPT-4, Claude, etc.) without redesigning workflows","Configure model parameters (temperature, max tokens) per node for fine-grained control","Use multiple different models in a single workflow for specialized tasks","Manage API credentials securely across different provider accounts"],"best_for":["Teams evaluating multiple LLM providers without workflow redesign","Applications requiring model fallback or multi-model ensembles","Organizations with existing relationships with specific LLM vendors"],"limitations":["No built-in request batching or caching — each node makes independent API calls","Provider-specific features (vision, function calling) require custom node implementations","Token counting approximations may not match actual provider billing"],"requires":["API key for at least one supported LLM provider","Model identifier matching provider's available models (e.g., 'gpt-4-turbo', 'claude-3-opus')"],"input_types":["system prompt (string)","user message (string)","model configuration (temperature, max_tokens, etc.)","chat history (array of messages)"],"output_types":["LLM response text","token usage (input/output counts)","model metadata (name, version)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_3","uri":"capability://planning.reasoning.control.flow.nodes.for.conditionals.loops.and.branching","name":"control flow nodes for conditionals, loops, and branching","description":"Provides specialized node types for implementing conditional logic (if/else), loops (for, while), and parallel execution within graphs. These nodes evaluate expressions or conditions at runtime and route execution to different downstream nodes based on results. Loop nodes iterate over arrays or ranges, executing a subgraph for each iteration and collecting results. Parallel nodes execute multiple branches concurrently and merge outputs.","intents":["Implement conditional logic in AI workflows (e.g., route to different LLM prompts based on input classification)","Iterate over lists of items and process each with the same workflow step","Execute multiple independent tasks in parallel and combine results","Build complex multi-branch decision trees without writing code"],"best_for":["Workflows with dynamic branching based on LLM outputs or data conditions","Batch processing tasks (e.g., classify 100 documents in a loop)","Multi-step reasoning chains with conditional next steps"],"limitations":["Loop iterations are sequential by default — no built-in parallel iteration within a loop","Nested loops and conditionals can become visually complex on the canvas","Expression evaluation limited to simple comparisons and boolean logic — no complex custom functions"],"requires":["Graph definition with control flow nodes connected to other nodes","Expressions written in Rivet's expression language (simple JavaScript-like syntax)"],"input_types":["condition expression (string)","loop array or range (array or number)","branch input data (any type)"],"output_types":["selected branch output (any type)","loop iteration results (array)","merged parallel outputs (array or object)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_4","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.tool.calling","name":"mcp (model context protocol) integration for tool calling","description":"Integrates with Model Context Protocol servers to expose external tools and resources as callable functions within LLM nodes. MCP nodes in the graph connect to MCP servers (local or remote), discover available tools, and allow LLM nodes to invoke them with automatic schema-based argument marshaling. Responses from tools are returned to the LLM for further processing.","intents":["Enable LLMs to call external APIs and tools (web search, database queries, file operations) from within workflows","Integrate with MCP servers (Anthropic's ecosystem) for standardized tool discovery and invocation","Build agentic workflows where LLMs can autonomously decide which tools to use","Expose custom tools to LLMs without writing provider-specific function calling code"],"best_for":["Teams building agentic AI systems with tool use","Organizations with existing MCP server infrastructure","Workflows requiring dynamic tool discovery and invocation"],"limitations":["MCP server must be running and accessible (local or network) — no built-in server management","Tool schemas must be properly defined in MCP servers — malformed schemas cause failures","No automatic retry or error handling for tool invocations — requires explicit error nodes"],"requires":["MCP server running and accessible (localhost or remote URL)","MCP server configuration in Rivet project settings","LLM provider supporting tool/function calling (OpenAI, Anthropic)"],"input_types":["MCP server connection details (host, port, or URL)","tool name and arguments (strings, objects)","LLM response with tool calls"],"output_types":["tool execution results (any type)","tool error messages (string)","LLM response after tool use"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_5","uri":"capability://data.processing.analysis.data.transformation.and.processing.nodes","name":"data transformation and processing nodes","description":"Provides nodes for common data manipulation tasks: JSON parsing/serialization, string operations (split, join, replace), array operations (map, filter, reduce), object manipulation, and type conversions. These nodes operate on graph data flowing between LLM calls and other processing steps. Transformations are configured through node UI panels or expression language for custom logic.","intents":["Parse and extract structured data from LLM text outputs (JSON, CSV, etc.)","Transform data between workflow steps (e.g., format API responses for next LLM call)","Aggregate or filter results from loops or parallel branches","Convert between data types (string to array, object to JSON, etc.)"],"best_for":["Workflows with complex data pipelines between LLM calls","Batch processing tasks requiring data aggregation","Integration scenarios where API response formats don't match LLM input expectations"],"limitations":["Expression language limited to simple operations — no complex custom functions or imports","Large data transformations (100k+ items) may cause performance issues in browser execution","No streaming or chunked processing — entire dataset loaded into memory"],"requires":["Input data in supported format (JSON, string, array, object)","Expression or transformation configuration in node UI"],"input_types":["JSON (objects, arrays)","strings","numbers","arrays","objects"],"output_types":["transformed JSON","strings","arrays","objects","numbers"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_6","uri":"capability://automation.workflow.trivet.testing.framework.for.workflow.validation","name":"trivet testing framework for workflow validation","description":"Built-in testing framework for validating graph behavior through test cases. Tests define input datasets, expected outputs, and assertions. The framework executes graphs against test data, compares actual outputs to expected values, and generates test reports. Tests can be run in the desktop IDE or via CLI for CI/CD integration.","intents":["Validate that graph outputs match expected results before production deployment","Regression test workflows when updating prompts or logic","Generate test coverage reports for AI workflows","Integrate workflow testing into CI/CD pipelines"],"best_for":["Teams deploying AI workflows to production and requiring quality gates","Organizations with compliance requirements for AI system validation","Developers iterating on prompts and needing quick feedback on changes"],"limitations":["Tests require pre-defined expected outputs — no fuzzy matching or semantic similarity scoring","LLM API calls in tests consume tokens and incur costs — no built-in mocking","Test data must be manually curated — no automatic test generation from logs"],"requires":["Test dataset with inputs and expected outputs","Graph definition to test","API credentials for any LLM calls in the graph"],"input_types":["test case definitions (JSON)","input datasets (arrays of objects)","expected output values (any type)"],"output_types":["test results (pass/fail per case)","test report (summary, failures, execution time)","assertion error messages (string)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_7","uri":"capability://automation.workflow.execution.recording.and.replay.for.auditing","name":"execution recording and replay for auditing","description":"Automatically records all graph executions, capturing node inputs, outputs, execution order, and timing. Recordings are stored as structured logs and can be replayed to inspect execution state at any point. Integrates with Gentrace for observability and cost tracking across LLM calls.","intents":["Audit what happened during a graph execution for debugging or compliance","Replay an execution to inspect variable values at specific nodes","Track LLM API costs and token usage across workflow executions","Generate execution reports for stakeholders or compliance audits"],"best_for":["Organizations with compliance or audit requirements for AI systems","Teams debugging production issues by replaying recorded executions","Cost-conscious teams tracking LLM spending per workflow"],"limitations":["Recordings stored in memory or local storage — no built-in cloud persistence","Large graphs with many iterations generate large recording files","Replay is read-only — cannot modify execution state and re-run"],"requires":["Graph execution to record","Storage for execution logs (local file system or cloud storage)","Optional: Gentrace API key for observability integration"],"input_types":["graph execution trace (internal format)","node execution events (input, output, timing)"],"output_types":["execution recording (JSON or binary format)","execution report (summary, timeline, costs)","Gentrace observability data (if integrated)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_8","uri":"capability://tool.use.integration.plugin.system.for.extending.node.types.and.functionality","name":"plugin system for extending node types and functionality","description":"Extensible plugin architecture allowing developers to create custom node types and register them with Rivet. Plugins implement a standard interface defining node inputs, outputs, configuration, and execution logic. Plugins can be loaded from npm packages or local files and are available in the node palette for use in graphs.","intents":["Add custom node types for domain-specific operations (e.g., specialized NLP, domain APIs)","Integrate proprietary tools or internal APIs as nodes in workflows","Extend Rivet with organization-specific functionality without forking the codebase","Build reusable node libraries for specific use cases (e.g., document processing, data validation)"],"best_for":["Organizations with custom tools or APIs requiring integration into workflows","Teams building domain-specific AI applications (legal, medical, financial)","Developers creating reusable node libraries for their industry"],"limitations":["Plugin development requires TypeScript/JavaScript knowledge — not no-code","Plugins must be manually installed and configured — no plugin marketplace or auto-discovery","Plugin security not enforced — malicious plugins can access system resources"],"requires":["Node.js development environment","TypeScript knowledge for plugin development","Rivet plugin SDK (@ironclad/rivet-core)"],"input_types":["plugin definition (TypeScript class)","node configuration (any type)","node input values (any type)"],"output_types":["registered node type","node execution results (any type)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__cap_9","uri":"capability://automation.workflow.project.and.dataset.management.with.serialization","name":"project and dataset management with serialization","description":"Manages Rivet projects as collections of graphs, datasets, and configuration. Projects are serialized to JSON files for version control and sharing. Datasets are collections of test inputs and expected outputs used for testing and batch processing. Project structure includes graph definitions, dataset definitions, and project settings (API keys, model configurations).","intents":["Organize multiple related graphs and datasets in a single project","Share workflow projects with team members via file export/import","Version control graph definitions and datasets in Git","Manage test datasets for validation and batch processing"],"best_for":["Teams collaborating on multiple related AI workflows","Organizations requiring version control and audit trails for AI systems","Projects with large test datasets requiring organized management"],"limitations":["No built-in Git integration — requires manual export/import for version control","No collaborative editing — only one user can edit a project at a time","Project files can become large (100MB+) with many graphs or large datasets"],"requires":["File system access for project storage","JSON serialization format for project files"],"input_types":["graph definitions (JSON)","dataset definitions (JSON)","project settings (JSON)"],"output_types":["project file (JSON)","exported graphs (JSON)","exported datasets (CSV or JSON)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rivet__headline","uri":"capability://app.builders.visual.programming.framework.for.ai.agent.development","name":"visual programming framework for ai agent development","description":"Rivet is an open-source visual programming environment designed for building, testing, and debugging complex AI workflows and agent chains using a node-based editor, making it ideal for developers looking to create LLM-based applications.","intents":["best visual programming framework","visual programming for AI agents","node-based editor for AI workflows","AI agent development tools","best tools for designing LLM workflows"],"best_for":["developers creating AI agents","teams building LLM applications"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["app-builders"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Tauri runtime (Windows, macOS, Linux)","At least 4GB RAM for smooth graph rendering with 50+ nodes","API keys for LLM providers (OpenAI, Anthropic, etc.)","Node.js 18+ for @ironclad/rivet-node execution","Graph definition in Rivet JSON format","API credentials for any LLM nodes in the graph","Prompt text (string)","Sample data for variable interpolation (objects or arrays)","LLM API credentials for testing","Node.js 18+ runtime"],"failure_modes":["Desktop-only application (Tauri) — no web-based collaborative editing","Graph complexity scales visually; very large graphs (100+ nodes) become difficult to navigate","No built-in version control — requires manual export/import for collaboration","Local execution blocks on LLM API calls — no built-in async batching or request pooling","Process context stored in memory — no persistence across restarts without explicit serialization","Remote debugging requires active debugger connection; no async replay from logs","Prompt designer is separate from graph execution — requires manual copy/paste to integrate","No built-in prompt optimization or quality scoring — relies on manual evaluation","Variable interpolation limited to simple string substitution — no complex templating","Server mode requires manual API endpoint configuration — no auto-generated OpenAPI specs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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=rivet","compare_url":"https://unfragile.ai/compare?artifact=rivet"}},"signature":"u2C04su8z3dxaSJdmLEGfMCGlBlmKi0kM6r2NlMK45Peyz6XHzKPvbmoXaSLFLL4cT7iWATddCBhtA0zro/4Bw==","signedAt":"2026-06-20T16:03:41.454Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rivet","artifact":"https://unfragile.ai/rivet","verify":"https://unfragile.ai/api/v1/verify?slug=rivet","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"}}