Lamatic.ai

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.