Lindy AI

Lindy provides a no-code visual canvas where users drag pre-built action blocks (triggers, conditions, integrations) and connect them with data flow lines to construct multi-step automation sequences. The builder abstracts away API authentication, request formatting, and error handling by presenting simplified UI forms for each integration, automatically translating user selections into backend API calls and conditional logic without requiring code generation or manual API documentation review.

Lindy offers a library of pre-configured workflow templates (customer support bot, lead qualification, email responder, etc.) that bundle together trigger logic, LLM prompts, integration steps, and error handling into a single deployable unit. Users can clone a template, customize prompts and connected apps, and launch without building from scratch, reducing time-to-automation from hours to minutes for standard use cases.

Lindy maintains a context object that persists data across workflow steps, allowing users to store and reference variables (workflow inputs, step outputs, computed values) throughout execution. Variables can be set explicitly in steps or automatically captured from previous step outputs, and referenced in downstream steps using template syntax (e.g., {{variable_name}}). This enables data reuse and reduces redundant API calls by caching intermediate results.

Lindy supports workflow creation and execution in multiple languages, with UI localization and support for non-English prompts and data processing. The platform can handle multilingual input data and route to language-specific processing steps, enabling teams to build workflows that serve international customers without language barriers.

Lindy provides controls to limit workflow execution frequency and API call volume, preventing runaway costs from excessive LLM usage or API calls. Users can set execution caps (max runs per day/month), step-level rate limits, and cost budgets that pause workflows when thresholds are exceeded. This prevents surprise bills from high-volume automation or LLM token consumption.

Lindy maintains a catalog of 500+ pre-built connectors (Slack, Gmail, Salesforce, HubSpot, Stripe, etc.) with built-in OAuth 2.0 and API key handling that abstracts authentication complexity. When a user selects an app in the workflow builder, Lindy handles the full OAuth redirect flow, securely stores encrypted credentials in its backend, and automatically refreshes tokens, eliminating manual API key management and reducing security risks from hardcoded credentials.

Lindy embeds LLM capabilities (via OpenAI, Anthropic, or proprietary models) directly into workflow steps, allowing users to write natural language prompts in a text field that get executed against incoming data. The platform abstracts provider selection and model switching, automatically formatting context (previous step outputs, workflow variables) as LLM input and parsing structured outputs (JSON, classifications) without requiring users to write prompt engineering code or manage API calls directly.

Lindy supports multiple trigger types (webhook, scheduled cron, app event, manual) that initiate workflow execution. When a trigger fires, the platform queues the execution, runs steps sequentially or in parallel based on workflow design, and implements automatic retry logic with exponential backoff for failed API calls. Execution state (running, completed, failed) is tracked and logged, with failed executions optionally retried after a delay without user intervention.

Lindy provides a data mapper interface that allows users to visually map output fields from one step to input fields of the next step, with support for simple transformations (concatenation, case conversion, date formatting) without writing code. The mapper displays available fields from the previous step's output and lets users drag-and-drop or select fields to populate downstream step inputs, automatically handling type coercion and null value defaults.

Lindy allows users to add conditional branches (if-then-else) to workflows by defining conditions on step outputs (e.g., 'if priority equals high, route to urgent queue; else route to standard queue'). Conditions are built using a visual rule builder with operators (equals, contains, greater than, regex match) applied to workflow variables or step outputs, automatically routing execution to different downstream steps based on condition evaluation without requiring code.

Lindy tracks all workflow executions with detailed logs showing step-by-step execution flow, input/output data, error messages, and timestamps. Users can view execution history, filter by status (success, failed, running), and inspect individual execution logs to debug failures. The platform maintains an audit trail of workflow changes and executions, enabling troubleshooting and compliance tracking without external logging infrastructure.

Lindy allows users to save workflow versions and manage deployments, enabling testing before production activation. Users can create a draft version, test it with sample data, and publish to production when ready. The platform tracks version history and allows rollback to previous versions if needed, reducing risk of breaking changes in live automations.

Lindy generates unique webhook URLs for workflows that accept inbound HTTP POST requests with JSON payloads, enabling external systems to trigger workflows with custom data. Users can define expected payload schema, and Lindy automatically parses incoming data and makes it available to workflow steps. This enables form submissions, third-party system callbacks, and real-time event ingestion without polling or manual data entry.