{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_lamatic-ai","slug":"lamatic-ai","name":"Lamatic.ai","type":"product","url":"https://lamatic.ai","page_url":"https://unfragile.ai/lamatic-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_lamatic-ai__cap_0","uri":"capability://automation.workflow.visual.workflow.builder.for.multi.step.ai.chains","name":"visual workflow builder for multi-step ai chains","description":"Provides a drag-and-drop interface for constructing sequential and branching AI workflows without code, where users connect nodes representing LLM calls, data transformations, and conditional logic. The builder likely uses a DAG (directed acyclic graph) model to represent workflow topology, with visual node types for prompts, function calls, loops, and branching. State flows between nodes as JSON payloads, enabling complex multi-step agent behaviors like retrieval-augmented generation pipelines or iterative refinement loops.","intents":["I want to build a chatbot that calls multiple APIs and processes responses without writing backend code","I need to create a content generation pipeline that chains multiple LLM calls with data validation between steps","I want to prototype an AI agent that makes decisions based on tool outputs without deploying custom code"],"best_for":["non-technical business users and product managers prototyping AI workflows","small teams without dedicated ML/backend engineers","rapid MVP development where time-to-market outweighs optimization"],"limitations":["Visual builders typically add cognitive overhead for complex workflows with >10 nodes; readability degrades compared to code","Debugging multi-step workflows in UI is slower than code-based approaches with IDE integration","No version control or diff visualization for workflow changes; collaboration requires external tooling","Performance optimization (caching, parallelization) may be limited to preset options rather than custom logic"],"requires":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Basic understanding of workflow logic (sequences, conditionals, loops)"],"input_types":["text prompts","structured JSON data","API responses","user input from forms"],"output_types":["text (LLM responses)","structured JSON","API call results","formatted documents"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_1","uri":"capability://tool.use.integration.native.llm.provider.integration.with.function.calling","name":"native llm provider integration with function calling","description":"Abstracts away provider-specific API differences (OpenAI, Anthropic, Cohere, etc.) through a unified interface, allowing users to swap LLM providers without rebuilding workflows. Implements function calling (tool use) by translating user-defined function schemas into provider-native formats (OpenAI's function_call, Anthropic's tool_use, etc.), handling request/response marshaling and retry logic transparently. Likely uses a schema registry pattern where functions are defined once and automatically adapted to each provider's calling convention.","intents":["I want to switch from OpenAI to Anthropic without rewriting my workflow","I need my AI agent to call external APIs (weather, CRM, database) as part of its reasoning","I want to use function calling without learning each provider's API format"],"best_for":["teams building multi-provider AI applications for cost optimization or redundancy","developers avoiding vendor lock-in to a single LLM provider","non-technical users who need agent-like tool use without understanding function calling mechanics"],"limitations":["Provider-specific features (vision, structured output, extended thinking) may not be fully exposed through the abstraction layer","Latency overhead from schema translation and marshaling; estimated 50-150ms per function call","Function calling reliability depends on provider's implementation; some providers have higher hallucination rates for tool use","No built-in cost optimization across providers; users must manually compare pricing and switch providers"],"requires":["API keys for at least one supported LLM provider","Function definitions in JSON schema format or UI-based schema builder","Network connectivity to provider APIs"],"input_types":["JSON schema definitions","function signatures","provider API credentials"],"output_types":["function call results","provider-normalized responses","execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_10","uri":"capability://automation.workflow.execution.monitoring.and.analytics","name":"execution monitoring and analytics","description":"Provides dashboards showing workflow execution metrics (success rate, average latency, cost per run, error rates) and detailed logs for each execution. Likely includes filtering and search capabilities to find specific runs by date, status, or parameters. Analytics may show trends over time (e.g., 'success rate declined 5% this week') and identify bottlenecks (e.g., 'node X takes 2s on average'). Execution data is probably retained for 30-90 days with optional export for long-term analysis.","intents":["I want to see how many times my workflow ran today and how much it cost","I need to find and debug a specific failed execution","I want to identify which step in my workflow is slowest"],"best_for":["teams monitoring production workflows for reliability and cost","users optimizing workflow performance and cost","compliance scenarios requiring execution audit trails"],"limitations":["Execution data retention is limited (30-90 days); long-term analysis requires export","Real-time dashboards may have latency (5-15 minutes) before data appears","Analytics are limited to platform-provided metrics; custom metrics require external tools","No built-in alerting; users must manually check dashboards or set up external monitoring"],"requires":["Active workflow with execution history","Access to the monitoring dashboard"],"input_types":["execution logs","performance metrics"],"output_types":["dashboards","analytics reports","execution logs","cost breakdowns"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_11","uri":"capability://automation.workflow.multi.user.collaboration.and.permissions","name":"multi-user collaboration and permissions","description":"Enables multiple team members to work on the same workflow with role-based access control (viewer, editor, admin). Likely supports real-time collaboration with conflict resolution, or asynchronous workflows with change notifications. Permissions probably control who can edit, deploy, or view execution logs. The platform may support team workspaces where workflows are shared and organized by project.","intents":["I want my team to collaborate on building a workflow without overwriting each other's changes","I need to give my colleague read-only access to see how my workflow performs","I want to restrict who can deploy workflows to production"],"best_for":["teams building AI workflows collaboratively","organizations requiring access control and audit trails","enterprises with governance requirements"],"limitations":["Real-time collaboration may have latency or conflict resolution issues; asynchronous workflows are safer but slower","Permission model is likely simple (viewer/editor/admin); fine-grained permissions may not be available","No built-in audit logging of who changed what; external tools may be needed for compliance","Concurrent editing conflicts may require manual resolution"],"requires":["Team workspace or shared organization account","Multiple user accounts with appropriate permissions"],"input_types":["user invitations","permission assignments"],"output_types":["shared workflows","access logs","collaboration notifications"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_12","uri":"capability://code.generation.editing.custom.code.execution.nodes","name":"custom code execution nodes","description":"Allows advanced users to write custom code (likely Python or JavaScript) within workflow nodes for logic that cannot be expressed visually. Code nodes are sandboxed and have access to the workflow context (previous node outputs, input parameters). Execution is probably isolated from the main platform to prevent security issues. Code nodes can return structured data that flows to subsequent nodes in the DAG.","intents":["I need to implement complex business logic that the visual builder cannot express","I want to use a Python library that is not available as a pre-built node","I need to transform data in a way that requires custom code"],"best_for":["developers with coding skills building hybrid no-code/code workflows","teams with specialized logic that requires custom implementation","advanced users extending the platform's capabilities"],"limitations":["Code execution adds latency (100-500ms per node) due to sandboxing overhead","Limited library support; only whitelisted libraries are available in the sandbox","Debugging code nodes is harder than local development; error messages may be limited","Code nodes break the no-code abstraction; they require technical expertise and introduce maintenance burden","Execution timeout limits (likely 30-60 seconds) prevent long-running operations"],"requires":["Programming knowledge (Python or JavaScript)","Understanding of the workflow context and data flow","Familiarity with the platform's code node API"],"input_types":["Python or JavaScript code","workflow context (previous outputs, parameters)"],"output_types":["structured data","execution results","error logs"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_2","uri":"capability://automation.workflow.freemium.workflow.execution.with.usage.based.pricing","name":"freemium workflow execution with usage-based pricing","description":"Offers a free tier allowing unlimited workflow creation and testing with capped monthly execution limits (likely 1000-5000 runs), then transitions to pay-as-you-go pricing based on workflow runs, LLM tokens consumed, or API calls made. Execution costs are typically transparent and itemized per workflow, enabling users to monitor spending and optimize expensive chains. The platform likely meters execution at the workflow-run level, tracking token usage from each LLM provider and passing through provider costs plus platform markup.","intents":["I want to prototype an AI workflow without upfront commitment or credit card","I need to understand the true cost of my AI application before scaling","I want to pay only for what I use, not fixed monthly seats"],"best_for":["startups and small teams with variable AI workload and limited budgets","non-technical users experimenting with AI before committing resources","cost-conscious teams building internal tools with unpredictable usage"],"limitations":["Free tier execution limits may be insufficient for production workloads; scaling requires paid plan","Pay-as-you-go pricing can become expensive at scale (100k+ monthly runs); no volume discounts mentioned","Costs are opaque if platform markup on LLM tokens is not clearly disclosed; users may be surprised by bills","No built-in cost controls or spending caps; runaway workflows could incur unexpected charges"],"requires":["Valid email for account creation","Payment method (credit card) for paid tier","LLM provider API keys (costs passed through to user)"],"input_types":["workflow definitions","execution parameters"],"output_types":["execution results","usage metrics","cost breakdowns"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_3","uri":"capability://automation.workflow.workflow.testing.and.debugging.in.visual.environment","name":"workflow testing and debugging in visual environment","description":"Provides in-platform testing capabilities where users can execute workflows with test data, inspect intermediate outputs at each node, and view execution logs without deploying to production. Likely includes a step-through debugger showing LLM prompts sent, responses received, and function call results. Test runs may be free or discounted compared to production execution, enabling rapid iteration. The platform probably stores execution history with full request/response payloads for post-mortem analysis.","intents":["I want to test my workflow with sample data before deploying it to users","I need to see what prompt was actually sent to the LLM and why it failed","I want to debug a multi-step workflow without running it end-to-end each time"],"best_for":["non-technical users who need visibility into AI behavior without reading API logs","teams iterating on prompt engineering and workflow logic","developers troubleshooting complex multi-step chains"],"limitations":["Visual debugging may be slower than code-based debugging with IDE breakpoints for complex workflows","Execution history storage is likely limited (30-90 days); long-term audit trails require export","Test data must be manually entered or uploaded; no built-in test data generation or fixtures","Debugging latency depends on LLM provider response times; no local simulation or mocking of LLM responses"],"requires":["Active workflow in the platform","Test data in compatible format (JSON, CSV, or form input)","LLM provider API keys with sufficient quota"],"input_types":["test data (JSON, CSV, text)","workflow parameters"],"output_types":["execution results","step-by-step logs","LLM prompts and responses","function call details"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_4","uri":"capability://automation.workflow.workflow.deployment.and.hosting","name":"workflow deployment and hosting","description":"Enables one-click deployment of tested workflows to a managed hosting environment, generating a public or private API endpoint that can be called by external applications. Likely handles scaling, load balancing, and request queuing automatically. Workflows may be exposed as REST APIs, webhooks, or embedded chat interfaces. The platform probably manages infrastructure provisioning and monitoring, abstracting away DevOps concerns from users.","intents":["I want to deploy my AI workflow as an API without setting up servers","I need to expose my chatbot as a web widget on my website","I want to trigger my workflow from another application via webhook"],"best_for":["non-technical users deploying AI applications without DevOps expertise","small teams avoiding infrastructure management overhead","rapid prototyping where time-to-deployment is critical"],"limitations":["Vendor lock-in; workflows are tightly coupled to Lamatic.ai infrastructure and cannot be easily exported","Scaling limits unknown; platform may have undisclosed rate limits or concurrency caps","No built-in CDN or edge deployment; latency depends on Lamatic.ai's server locations","Monitoring and alerting likely limited compared to enterprise platforms; no SLA guarantees mentioned","Cold start latency for serverless-style execution may be 1-3 seconds per request"],"requires":["Completed and tested workflow","Active Lamatic.ai account with sufficient execution quota","Optional: custom domain configuration"],"input_types":["workflow definition","deployment configuration"],"output_types":["API endpoint URL","webhook configuration","embed code for chat widgets"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_5","uri":"capability://text.generation.language.prompt.engineering.and.template.library","name":"prompt engineering and template library","description":"Provides a library of pre-built prompt templates for common use cases (customer support, content generation, data extraction, etc.) that users can customize without writing prompts from scratch. Likely includes prompt versioning, A/B testing capabilities to compare prompt variants, and analytics showing which prompts perform best. The platform may offer prompt optimization suggestions based on execution history (e.g., 'this prompt has high token usage; consider simplifying'). Templates are probably community-contributed or curated by Lamatic.ai.","intents":["I want to use a proven prompt template for customer support instead of writing my own","I need to test two different prompts and see which one produces better results","I want suggestions for improving my prompt's efficiency and output quality"],"best_for":["non-technical users without prompt engineering expertise","teams rapidly iterating on prompt quality","users optimizing for cost or latency"],"limitations":["Template quality depends on community contributions; no guarantee of best practices","A/B testing requires sufficient traffic to reach statistical significance; low-volume workflows may not yield meaningful results","Prompt optimization suggestions are heuristic-based; may not account for domain-specific requirements","Limited to templates in the library; highly specialized use cases may require custom prompts"],"requires":["Active workflow in the platform","Sufficient execution history for A/B testing (100+ runs recommended)"],"input_types":["template selection","customization parameters","test variants"],"output_types":["prompt text","A/B test results","optimization recommendations"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_6","uri":"capability://data.processing.analysis.data.transformation.and.extraction.nodes","name":"data transformation and extraction nodes","description":"Provides visual nodes for common data operations (JSON parsing, CSV transformation, text extraction, field mapping) without requiring code. Likely uses a schema-based approach where users define input/output structures visually, and the platform generates transformation logic. May include regex-based text extraction, XPath/JSONPath queries, and conditional field mapping. These nodes integrate seamlessly into the workflow DAG, allowing data to flow between LLM calls and external APIs.","intents":["I want to extract specific fields from an API response and pass them to the next LLM call","I need to transform CSV data into a format my workflow can process","I want to parse unstructured text from an LLM response and extract structured data"],"best_for":["non-technical users building data pipelines without SQL or Python","teams integrating multiple APIs with different data formats","rapid prototyping where data transformation logic is secondary to AI logic"],"limitations":["Visual transformation builders are less flexible than code; complex transformations may require custom code nodes","Performance overhead for large datasets (>10MB); no built-in streaming or chunking","Limited to predefined transformation types; custom logic requires external code execution","No built-in data validation or schema enforcement; garbage-in-garbage-out if input data is malformed"],"requires":["Input data in supported format (JSON, CSV, XML, plain text)","Schema definition for output structure"],"input_types":["JSON","CSV","XML","plain text","API responses"],"output_types":["JSON","CSV","structured data","formatted text"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_7","uri":"capability://planning.reasoning.conditional.branching.and.loop.control","name":"conditional branching and loop control","description":"Enables workflows to branch based on conditions (if-then-else logic) and iterate over data (for-each loops, while loops) using a visual interface. Conditions are likely defined using a rule builder (e.g., 'if LLM response contains X, go to node Y'). Loops may iterate over arrays from API responses or user input. The platform probably supports nested branching and loops, though deeply nested structures may become difficult to visualize. Control flow is likely evaluated at runtime with full context from previous nodes.","intents":["I want my workflow to take different paths based on the LLM's response","I need to loop through a list of items and process each one with an LLM call","I want to retry a failed API call up to 3 times before giving up"],"best_for":["users building complex workflows with multiple decision points","batch processing scenarios requiring iteration over data","error handling and retry logic without custom code"],"limitations":["Visual representation of deeply nested branching becomes cluttered; readability degrades with >5 levels of nesting","Loop performance depends on iteration count; large loops (>1000 iterations) may timeout or incur high costs","Condition evaluation is sequential; no built-in parallelization of branches","Debugging complex branching logic is harder in UI than in code with IDE support"],"requires":["Workflow with multiple nodes to branch/loop over","Clear definition of branching conditions or loop data"],"input_types":["condition rules (if-then-else)","loop data (arrays, lists)","control flow parameters"],"output_types":["branched execution paths","loop iteration results","control flow logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_8","uri":"capability://tool.use.integration.api.integration.and.webhook.triggers","name":"api integration and webhook triggers","description":"Allows workflows to be triggered by incoming webhooks (e.g., from Zapier, custom applications, or scheduled events) and to call external APIs as part of the workflow. Webhook triggers likely support authentication (API keys, OAuth) and payload validation. API call nodes support common HTTP methods (GET, POST, PUT, DELETE) with header and body customization. The platform probably handles request/response marshaling, error handling, and retry logic automatically. Supports both synchronous (request-response) and asynchronous (fire-and-forget) API calls.","intents":["I want to trigger my AI workflow when a new email arrives in Gmail","I need my workflow to call my company's internal API to fetch customer data","I want to send the workflow result to a Slack channel via webhook"],"best_for":["teams integrating AI workflows with existing business systems","event-driven automation scenarios (email, form submissions, webhooks)","connecting to APIs without custom code"],"limitations":["Webhook payload size limits (likely 1-10MB); large payloads may be rejected","API rate limiting is not abstracted; users must handle provider rate limits manually","Authentication is limited to API keys and basic OAuth; complex auth flows may require custom code","No built-in request/response caching; repeated calls to the same API incur full latency and cost","Webhook retry logic is likely simple (exponential backoff); complex retry strategies require custom logic"],"requires":["Webhook URL or API endpoint to call","Authentication credentials (API key, OAuth token, etc.)","Understanding of API request/response format"],"input_types":["webhook payloads (JSON)","API request parameters","authentication credentials"],"output_types":["API responses (JSON, XML, plain text)","webhook delivery status","error logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_lamatic-ai__cap_9","uri":"capability://automation.workflow.workflow.versioning.and.rollback","name":"workflow versioning and rollback","description":"Maintains a version history of workflow definitions, allowing users to view changes, compare versions, and rollback to previous versions if needed. Likely stores snapshots of the entire workflow DAG with timestamps and optional change descriptions. Rollback is probably one-click, reverting the deployed workflow to a previous version without downtime. Version history may be limited to recent versions (e.g., last 30 days) to manage storage costs.","intents":["I accidentally changed my workflow and want to revert to the previous version","I want to see what changed between two versions of my workflow","I need to maintain multiple versions of my workflow for A/B testing"],"best_for":["teams iterating on workflows and needing safety nets","production workflows requiring audit trails and rollback capability","users experimenting with changes without fear of breaking live workflows"],"limitations":["Version history is likely limited to recent versions; old versions may be automatically pruned","No branching or merging; version control is linear (no Git-like workflows)","Rollback is all-or-nothing; cannot selectively revert individual nodes","Version comparison UI may be limited; complex diffs are hard to visualize"],"requires":["Active workflow with execution history","Sufficient storage quota for version snapshots"],"input_types":["workflow definition changes"],"output_types":["version history","version diffs","rollback confirmation"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Basic understanding of workflow logic (sequences, conditionals, loops)","API keys for at least one supported LLM provider","Function definitions in JSON schema format or UI-based schema builder","Network connectivity to provider APIs","Active workflow with execution history","Access to the monitoring dashboard","Team workspace or shared organization account","Multiple user accounts with appropriate permissions"],"failure_modes":["Visual builders typically add cognitive overhead for complex workflows with >10 nodes; readability degrades compared to code","Debugging multi-step workflows in UI is slower than code-based approaches with IDE integration","No version control or diff visualization for workflow changes; collaboration requires external tooling","Performance optimization (caching, parallelization) may be limited to preset options rather than custom logic","Provider-specific features (vision, structured output, extended thinking) may not be fully exposed through the abstraction layer","Latency overhead from schema translation and marshaling; estimated 50-150ms per function call","Function calling reliability depends on provider's implementation; some providers have higher hallucination rates for tool use","No built-in cost optimization across providers; users must manually compare pricing and switch providers","Execution data retention is limited (30-90 days); long-term analysis requires export","Real-time dashboards may have latency (5-15 minutes) before data appears","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.446Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=lamatic-ai","compare_url":"https://unfragile.ai/compare?artifact=lamatic-ai"}},"signature":"wrVbnaiSVB4Pi9wyg8YGE/iosN/jb+8AbzecQ7ADLU0qmcQgoGZAcVvhBWtz10JK5wPHPfGj+lstjfzRoBt1Cg==","signedAt":"2026-06-22T00:13:28.466Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lamatic-ai","artifact":"https://unfragile.ai/lamatic-ai","verify":"https://unfragile.ai/api/v1/verify?slug=lamatic-ai","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"}}