{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-fine-tuner","slug":"fine-tuner","name":"Fine Tuner","type":"platform","url":"https://fine-tuner.ai/","page_url":"https://unfragile.ai/fine-tuner","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-fine-tuner__cap_0","uri":"capability://automation.workflow.visual.agent.workflow.builder.with.drag.and.drop.node.composition","name":"visual agent workflow builder with drag-and-drop node composition","description":"Provides a no-code canvas interface where users assemble AI agents by connecting visual nodes representing tasks, decision points, and integrations. The builder likely uses a directed acyclic graph (DAG) execution model to chain operations, with node types pre-configured for common patterns (LLM calls, API invocations, data transformations, branching logic). Execution flow is validated at design time to prevent circular dependencies and invalid state transitions.","intents":["Build multi-step AI agent workflows without writing code","Visually design agent decision trees and conditional logic flows","Connect external APIs and data sources to agent execution pipelines","Prototype and iterate on agent behavior quickly without deployment friction"],"best_for":["Non-technical business users building automation workflows","Product managers prototyping agent-based features","Teams needing rapid iteration on agent logic without engineering overhead"],"limitations":["No-code abstraction may hide complexity for advanced conditional logic or recursive patterns","Visual canvas scalability degrades with workflows exceeding 50+ nodes","Limited ability to express domain-specific logic that doesn't fit pre-built node templates"],"requires":["Web browser with modern JavaScript support","Account creation on Fine Tuner / Synthflow platform","API credentials for any external integrations (OpenAI, Anthropic, custom APIs)"],"input_types":["visual node configuration","text prompts for LLM nodes","API endpoint URLs and authentication","structured data schemas for transformation nodes"],"output_types":["executable agent workflow definition","deployed agent endpoint","execution logs and trace data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.model.selection.and.routing","name":"multi-provider llm model selection and routing","description":"Abstracts LLM provider APIs (OpenAI, Anthropic, local models, etc.) behind a unified node interface, allowing users to swap models or route requests across providers without rebuilding workflows. Likely implements a provider adapter pattern with standardized request/response schemas, enabling cost optimization (routing expensive queries to cheaper models) and fallback logic (retry with alternative provider on failure).","intents":["Switch between different LLM providers without modifying agent logic","Route different agent tasks to cost-optimized models based on complexity","Implement fallback strategies when a primary LLM provider is unavailable","Compare model outputs across providers for quality evaluation"],"best_for":["Teams managing multi-model inference costs at scale","Builders wanting provider independence and avoiding vendor lock-in","Organizations with compliance requirements to use specific model providers"],"limitations":["Provider-specific features (vision, function calling, streaming) may not be uniformly exposed across all adapters","Latency variance across providers complicates SLA guarantees for agent responses","Token counting and cost estimation requires provider-specific logic, adding complexity"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to external LLM endpoints or local model server","Understanding of model capabilities and cost differences across providers"],"input_types":["model identifier string","prompt text","provider configuration (API endpoint, authentication)"],"output_types":["LLM completion text","token usage metadata","provider-specific response metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_2","uri":"capability://automation.workflow.agent.execution.and.state.management.with.persistence","name":"agent execution and state management with persistence","description":"Executes defined workflows with stateful tracking of intermediate results, variable bindings, and execution history. Implements a state machine or event-driven execution model where each node transition updates a context object passed through the workflow. Likely persists execution state to enable resumption after failures, audit trails, and debugging of agent behavior across multiple runs.","intents":["Run agent workflows reliably with automatic error recovery and retry logic","Debug agent behavior by inspecting intermediate state at each workflow step","Track execution history and audit logs for compliance and troubleshooting","Resume interrupted workflows from the last successful step without restarting"],"best_for":["Production agent deployments requiring reliability and observability","Teams needing audit trails for regulated workflows (finance, healthcare)","Builders debugging complex multi-step agent logic"],"limitations":["State persistence adds latency (typically 50-200ms per step) depending on storage backend","Long-running workflows with large intermediate state objects may hit memory or storage limits","State serialization complexity increases with custom data types or circular references"],"requires":["Persistent storage backend (database, cloud storage) for state snapshots","Network connectivity to state storage service","Sufficient storage quota for execution history retention"],"input_types":["workflow definition","initial context variables","execution parameters (timeout, retry count)"],"output_types":["final workflow result","execution trace with intermediate states","error logs and failure metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_3","uri":"capability://tool.use.integration.integration.with.external.apis.and.data.sources.via.node.connectors","name":"integration with external apis and data sources via node connectors","description":"Provides pre-built or custom node types that wrap external API calls, database queries, and webhook invocations into workflow steps. Likely uses a schema-based approach where API endpoints are introspected to generate input/output schemas, enabling type-safe parameter binding and response mapping without manual configuration. Supports authentication (API keys, OAuth, basic auth) managed at the platform level.","intents":["Fetch data from external APIs and inject it into agent decision-making","Write agent outputs back to databases, CRMs, or business systems","Trigger webhooks or downstream workflows based on agent decisions","Chain multiple API calls with data transformation between steps"],"best_for":["Builders connecting agents to existing business systems and data sources","Teams automating workflows that span multiple SaaS platforms","Organizations with REST or GraphQL APIs needing agent-driven automation"],"limitations":["Pre-built connectors only cover popular APIs; custom APIs require manual schema definition","API rate limits and timeouts require explicit handling in workflow logic","Authentication credential management adds security complexity (key rotation, scope management)"],"requires":["API endpoint URL and authentication credentials (API key, OAuth token, etc.)","API schema or documentation for parameter and response mapping","Network access from Fine Tuner platform to external API endpoints"],"input_types":["API endpoint configuration","request parameters (query, body, headers)","authentication credentials"],"output_types":["API response data (JSON, XML, etc.)","HTTP status and error metadata","transformed/mapped response data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_4","uri":"capability://text.generation.language.prompt.template.management.with.variable.substitution.and.versioning","name":"prompt template management with variable substitution and versioning","description":"Provides a prompt authoring interface where users define LLM prompts with variable placeholders (e.g., {{user_input}}, {{context}}) that are dynamically substituted at runtime from workflow context. Likely supports prompt versioning, allowing users to iterate on prompts and compare outputs across versions. May include prompt optimization suggestions or cost estimation based on token counts.","intents":["Author and iterate on LLM prompts without redeploying workflows","Reuse prompt templates across multiple agent tasks","A/B test different prompt variations to optimize agent output quality","Track prompt changes and rollback to previous versions if quality degrades"],"best_for":["Product teams optimizing agent output quality through prompt engineering","Builders managing multiple prompt variations for different use cases","Organizations needing audit trails of prompt changes for compliance"],"limitations":["No built-in evaluation framework for comparing prompt quality across versions","Variable substitution is simple string replacement; no support for complex templating logic (loops, conditionals)","Prompt versioning is manual; no automatic A/B testing or statistical significance testing"],"requires":["Understanding of prompt engineering best practices","Access to Fine Tuner prompt editor interface","Optional: evaluation dataset for testing prompt variations"],"input_types":["prompt text with variable placeholders","variable definitions and types","version metadata (description, author)"],"output_types":["rendered prompt with substituted variables","token count estimate","version history and comparison"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_5","uri":"capability://automation.workflow.agent.deployment.and.endpoint.hosting.with.auto.scaling","name":"agent deployment and endpoint hosting with auto-scaling","description":"Converts completed workflow definitions into deployed HTTP endpoints that can be invoked by external applications. Likely handles request routing, input validation, response formatting, and auto-scaling based on traffic. May support webhook-based invocation for asynchronous agent execution and result callbacks.","intents":["Deploy agent workflows as production-ready API endpoints","Expose agents to external applications or user-facing interfaces","Scale agent execution automatically based on request volume","Invoke agents asynchronously with result callbacks for long-running tasks"],"best_for":["Teams deploying agents to production without managing infrastructure","Builders integrating agents into existing applications via API","Organizations needing auto-scaling for variable agent workloads"],"limitations":["Deployment latency (cold start) may be 1-5 seconds for first request after idle period","Auto-scaling configuration is platform-managed; limited control over scaling policies","Endpoint availability depends on platform uptime; no multi-region failover by default"],"requires":["Completed workflow definition in Fine Tuner","API key or authentication token for endpoint access","Understanding of HTTP request/response formats"],"input_types":["HTTP POST request with JSON payload","query parameters for configuration overrides","authentication headers"],"output_types":["HTTP response with JSON result","execution metadata (latency, token usage)","error responses with diagnostic information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_6","uri":"capability://automation.workflow.execution.monitoring.and.analytics.dashboard","name":"execution monitoring and analytics dashboard","description":"Provides real-time and historical visibility into agent execution metrics including success rates, latency, cost (token usage), and error rates. Likely aggregates execution traces across all deployed agents and workflows, enabling filtering by time range, workflow, or error type. May include alerting for anomalies (sudden latency spikes, increased error rates).","intents":["Monitor agent health and performance in production","Identify bottlenecks and optimize slow workflows","Track LLM costs and optimize model selection based on usage patterns","Debug failures by inspecting execution traces and error logs"],"best_for":["DevOps and SRE teams managing production agent deployments","Product managers tracking agent quality and cost metrics","Builders optimizing agent performance and reliability"],"limitations":["Analytics dashboard may have latency (5-15 minutes) for real-time metrics aggregation","Historical data retention is limited (typically 30-90 days) depending on plan","Custom metrics or dimensions require manual configuration; limited flexibility for domain-specific KPIs"],"requires":["Active agent deployments generating execution data","Access to Fine Tuner analytics dashboard","Optional: alerting configuration (email, Slack, PagerDuty)"],"input_types":["execution traces from deployed agents","filter criteria (time range, workflow, status)"],"output_types":["aggregated metrics (success rate, latency, cost)","execution trace details","alert notifications"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fine-tuner__cap_7","uri":"capability://planning.reasoning.conditional.logic.and.branching.with.expression.evaluation","name":"conditional logic and branching with expression evaluation","description":"Enables workflow branching based on runtime conditions evaluated against workflow context variables. Likely supports simple expression syntax (comparisons, boolean operators) evaluated at workflow nodes to determine which downstream path to execute. May include support for loops or iteration over data collections.","intents":["Route agent execution based on LLM output or external data conditions","Implement different agent behaviors for different input types or user segments","Retry failed steps with exponential backoff or alternative strategies","Iterate over collections of data with per-item agent processing"],"best_for":["Builders creating complex multi-path agent workflows","Teams implementing conditional business logic without code","Organizations automating workflows with variable execution paths"],"limitations":["Expression syntax is limited to simple comparisons; no support for complex logic or custom functions","Loop constructs may have iteration limits to prevent infinite loops or resource exhaustion","Nested conditionals become difficult to visualize and maintain in the canvas UI"],"requires":["Understanding of workflow context variables and their types","Basic knowledge of boolean logic and comparison operators"],"input_types":["context variables","expression syntax (e.g., 'status == \"success\" AND score > 0.8')"],"output_types":["boolean result determining execution path","loop iteration metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Account creation on Fine Tuner / Synthflow platform","API credentials for any external integrations (OpenAI, Anthropic, custom APIs)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to external LLM endpoints or local model server","Understanding of model capabilities and cost differences across providers","Persistent storage backend (database, cloud storage) for state snapshots","Network connectivity to state storage service","Sufficient storage quota for execution history retention","API endpoint URL and authentication credentials (API key, OAuth token, etc.)"],"failure_modes":["No-code abstraction may hide complexity for advanced conditional logic or recursive patterns","Visual canvas scalability degrades with workflows exceeding 50+ nodes","Limited ability to express domain-specific logic that doesn't fit pre-built node templates","Provider-specific features (vision, function calling, streaming) may not be uniformly exposed across all adapters","Latency variance across providers complicates SLA guarantees for agent responses","Token counting and cost estimation requires provider-specific logic, adding complexity","State persistence adds latency (typically 50-200ms per step) depending on storage backend","Long-running workflows with large intermediate state objects may hit memory or storage limits","State serialization complexity increases with custom data types or circular references","Pre-built connectors only cover popular APIs; custom APIs require manual schema definition","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.039Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=fine-tuner","compare_url":"https://unfragile.ai/compare?artifact=fine-tuner"}},"signature":"KtZvUNnw0k6SEjjX+fLq91gY7c56iH2M9FukIpRQl5HZymr8STQ5blyYEsCBy3vMZ7QVmZtwxXiETJBo7EfHBw==","signedAt":"2026-06-20T11:43:36.178Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fine-tuner","artifact":"https://unfragile.ai/fine-tuner","verify":"https://unfragile.ai/api/v1/verify?slug=fine-tuner","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"}}