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LaunchDarkly Integration Guide

Connect LaunchDarkly feature management with enterprise release, incident, observability, and collaboration systems through REST APIs, GraphQL queries, and selected webhook events.

LaunchDarkly integration options at a glance

LaunchDarkly’s versioned REST API is the primary integration mechanism for projects, environments, feature flags, segments, members, audit logs, integrations, and administrative changes. Its GraphQL API supports selected read and nested-data queries, while REST remains important for mutations and resources outside the GraphQL schema. LaunchDarkly also supports webhook and integration notifications for selected changes rather than every event, plus bulk operations for selected feature-flag actions. Martini can securely consume these APIs, receive supported webhook requests, orchestrate approvals and synchronization workflows, map data to downstream systems, and handle asynchronous operations, pagination, retries, and duplicate deliveries.

Integration pointSupported by LaunchDarkly?Common use casesHow Martini supports it
REST APIsYesManage and retrieve projects, environments, feature flags, segments, members, audit logs, integrations, targeting rules, and variations.Martini can consume the versioned REST API, map responses, expose controlled internal APIs, and orchestrate administrative workflows.
GraphQL APIsYesQuery selected LaunchDarkly data with focused projections of nested project, environment, flag, or account information.Martini can consume supported GraphQL queries and transform the result; REST remains appropriate for unsupported resources and administrative mutations.
Webhooks / outbound callbacksLimitedReceive notifications for selected LaunchDarkly changes and event categories, depending on webhook or integration configuration.Martini can expose a webhook endpoint, validate requests, persist delivery identifiers where available, deduplicate events, and route normalized notifications.
Bulk / async operationsLimitedPerform selected bulk feature-flag actions that may execute asynchronously and produce per-item results.Martini can submit the operation, retain its identifier, poll status, inspect partial failures, and report completion accurately.
AuthenticationYesUse personal access tokens, service tokens, or OAuth 2.0 for REST access, with role and resource permissions controlling actions.Martini can store credentials as secured environment values and apply authenticated API calls with controlled workflow authorization.
SDKsYesEvaluate feature flags at application runtime through server-side, client-side, mobile, and other LaunchDarkly SDKs.Martini can coordinate services that use SDKs, but direct integration workflows should normally use LaunchDarkly APIs for administrative operations.
File / attachment APIsNot confirmedNo general-purpose LaunchDarkly file import, export, or attachment API was verified.Martini should use documented APIs, webhooks, SDK-based services, or supported third-party mechanisms instead of assuming file transfer.
Database / analytics accessNot confirmedNo direct customer database or general-purpose SQL access was verified.Martini can consume documented APIs or supported data-export mechanisms and write normalized data to an approved downstream database.

How LaunchDarkly exposes data and business events

LaunchDarkly REST APIs

LaunchDarkly’s versioned REST API is the principal mechanism for retrieving and managing projects, environments, feature flags, segments, members, audit logs, integrations, targeting rules, and variations.

Martini implementation pattern

Martini implementation pattern: a workflow authenticates with a secured service token, calls the required REST resource, validates the response, maps it to a canonical model, applies business rules, and writes to or reads from the target system. Administrative mutations should retrieve current state first when concurrent changes matter.

Implementation sequence

Receive an API request or scheduled trigger
Authenticate with a least-privilege LaunchDarkly service token
Retrieve the current LaunchDarkly resource
Validate project, environment, flag, and permission rules
Map and transform the response or mutation payload
Apply the authorized REST operation and persist an audit result

LaunchDarkly GraphQL API

LaunchDarkly documents GraphQL for selected queries where focused projections of nested project, environment, flag, or account data are useful. It is not a replacement for REST across all resources or mutations.

Martini implementation pattern

Martini implementation pattern: a workflow sends a supported GraphQL query, handles the response and possible errors, and maps the selected projection to a downstream schema. REST is used when the required resource or administrative mutation is not represented in the GraphQL schema.

Implementation sequence

Start a scheduled or API-driven query workflow
Authenticate the GraphQL request securely
Submit a supported query with required variables
Validate the response and handle GraphQL errors
Map nested results to the canonical model
Write the projection to the target system

LaunchDarkly Webhooks

LaunchDarkly supports webhook and integration notifications for selected changes and event categories. Coverage depends on configuration and should not be treated as a complete event stream.

Martini implementation pattern

Martini implementation pattern: an exposed Martini API receives the notification, validates the request, records a stable delivery or event identifier when available, acknowledges or persists the event, and routes eligible changes to downstream workflows. Scheduled reconciliation can provide completeness when notifications are selective.

Implementation sequence

Receive the supported LaunchDarkly webhook notification
Validate the request and configured event scope
Record the delivery identifier and raw event metadata
Suppress duplicate deliveries using idempotency controls
Enrich and map the event for the target system
Retry downstream delivery or send the event to reconciliation

LaunchDarkly Bulk Operations

LaunchDarkly provides bulk capabilities for selected feature-flag operations. Bulk actions may execute asynchronously and can include partial or per-item failures.

Martini implementation pattern

Martini implementation pattern: a workflow submits a permitted bulk action, stores the operation identifier, polls status at a controlled interval, evaluates individual results, and reports partial failure rather than treating the whole operation as successful.

Implementation sequence

Validate the requested bulk flag changes
Submit the supported bulk operation
Persist the returned operation identifier
Poll status with bounded backoff
Inspect per-item successes and failures
Publish the final result and retry eligible failures

Common LaunchDarkly integration patterns

Pattern 1: Govern feature-flag administration

When to use this pattern

Use this pattern when release or change-management systems must control LaunchDarkly mutations without giving every consuming application direct administrative access. It provides validation, approval checks, current-state comparison, auditability, and bounded retry behavior.

Integration direction
ServiceNow
Martini
LaunchDarkly
Example Mapping
LaunchDarkly FieldCanonical FieldTarget Field
changeNumberchangeIdworkflow reference
projectKeyprojectIdLaunchDarkly project
environmentKeyenvironmentIdLaunchDarkly environment
requestedVariationdesiredVariationflag variation
Martini implementation pattern

Martini receives an approved request, validates the project, environment, flag, variation, and caller authorization, retrieves current flag state, avoids overwriting unrelated targeting rules, performs the REST mutation, and returns the resulting state and audit information. Failed calls are retried only when safe and are recorded for operator review.

Martini capabilities used
  • APIs
  • workflows
  • data mapping
  • business rules
  • authentication and authorization
  • error handling

Pattern 2: Route LaunchDarkly changes to operations

When to use this pattern

Use this pattern when selected production flag changes should create notifications, incident context, or change updates in operational systems. It is appropriate for selective webhook coverage supplemented by reconciliation.

Integration direction
LaunchDarkly
Martini
Slack
Example Mapping
LaunchDarkly FieldCanonical FieldTarget Field
flagKeyfeatureFlagKeynotification subject
environmentKeyenvironmentchannel or routing rule
changeKindchangeTypeevent type
actorchangedByaudit context
Martini implementation pattern

Martini receives a supported webhook, validates and deduplicates it, filters out non-production changes when required, enriches the event with project and flag metadata, and sends a normalized notification. Downstream failures use bounded retries and a durable failure path.

Martini capabilities used
  • webhook consumption
  • workflows
  • data mapping
  • idempotency
  • conditional routing
  • error handling

Pattern 3: Synchronize the LaunchDarkly flag inventory

When to use this pattern

Use this pattern to maintain a governance catalog, reporting database, or lifecycle inventory of projects, environments, feature flags, segments, and selected metadata. Scheduled reconciliation helps detect missed notifications and stale flags.

Integration direction
LaunchDarkly
Martini
PostgreSQL
Example Mapping
LaunchDarkly FieldCanonical FieldTarget Field
projectIdsourceProjectIdlaunchdarkly_project_id
environmentKeyenvironmentKeylaunchdarkly_environment_key
flagKeyfeatureFlagKeylaunchdarkly_flag_key
temporaryisTemporarytemporary_flag
Martini implementation pattern

A scheduled Martini workflow follows pagination, retrieves the relevant collections, maps them into normalized tables, and performs upserts using stable LaunchDarkly identifiers. It can identify missing owners, expired lifecycle dates, or stale flags while preserving unknown fields for audit and replay.

Martini capabilities used
  • scheduling
  • API consumption
  • pagination handling
  • mapping and transformation
  • SQL database access
  • business rules

Pattern 4: Coordinate progressive release workflows

When to use this pattern

Use this pattern when deployment pipelines, approvals, or incident processes need to verify and update LaunchDarkly state as part of a controlled release. The workflow should protect production mutations with authorization and explicit approval.

Integration direction
Azure DevOps
Martini
LaunchDarkly
Example Mapping
LaunchDarkly FieldCanonical FieldTarget Field
pipelineIdreleaseIdaudit reference
flagKeyfeatureFlagKeyLaunchDarkly flag
deploymentStageenvironmentKeyLaunchDarkly environment
approvalStatusapprovedmutation gate
Martini implementation pattern

Martini consumes a release signal, verifies that the expected flag exists in the target environment, checks approval and current state, applies the requested targeting change through REST, and returns a success or actionable failure to the pipeline. Concurrent updates and partial bulk results are handled explicitly.

Martini capabilities used
  • API orchestration
  • workflow triggers
  • validation
  • business rules
  • data transformation
  • retry handling

Applications commonly integrated with LaunchDarkly

LaunchDarkly is commonly coordinated with development, change-management, collaboration, incident-management, observability, and deployment products. The relationships below describe integration patterns rather than verified native pairings; availability should be confirmed against the customer’s LaunchDarkly plan and current integration catalog.

Application Scenario Direction Martini Pattern
GitHub Associate feature-flag changes with pull requests, repositories, code ownership, and release workflows. GitHub → Martini → LaunchDarkly Martini receives an approved development or release signal, validates the project, environment, flag, and requested variation, then calls the LaunchDarkly REST API. Supported LaunchDarkly changes can also be normalized for GitHub-related workflows.
Jira Link feature-flag changes and approvals to issues, release work, and change-management records. Jira → Martini → LaunchDarkly A Martini workflow reads the approved Jira change, retrieves current LaunchDarkly flag state, applies governance rules, performs an authorized mutation, and sends the resulting state or audit information back to Jira.
Slack Notify engineering and release channels when selected production flag changes or approvals occur. LaunchDarkly → Martini → Slack Martini receives a supported LaunchDarkly webhook, validates and deduplicates it, filters by project and environment, enriches it with flag metadata, and posts a concise notification to the appropriate Slack destination.
PagerDuty Coordinate feature-flag changes with incidents, mitigations, and operational response. LaunchDarkly → Martini → PagerDuty Martini routes selected LaunchDarkly changes to PagerDuty and can accept an approved mitigation request, validate permissions and current state, then update a LaunchDarkly flag through REST.
Datadog Correlate flag changes with application metrics, monitors, and deployment visibility. LaunchDarkly → Martini → Datadog A Martini workflow maps LaunchDarkly notifications or scheduled inventory data into Datadog events and can use approved operational signals to coordinate controlled release actions.
New Relic Relate feature-flag changes to application performance and release analysis. LaunchDarkly → Martini → New Relic Martini transforms LaunchDarkly change information into New Relic-compatible event data, enriches it with project and environment context, and applies retry and duplicate controls.
ServiceNow Create or update change records and enforce enterprise release approvals around production flag changes. ServiceNow → Martini → LaunchDarkly Martini receives an approved ServiceNow change, validates the requested LaunchDarkly mutation, retrieves current flag state, applies the update, and returns audit or status information to ServiceNow.
Azure DevOps Connect work items, pipelines, and deployment stages with feature-flag state changes. Azure DevOps → Martini → LaunchDarkly A Martini workflow consumes deployment or approval signals, checks the expected LaunchDarkly project and environment, updates targeting when authorized, and returns status to the pipeline with bounded retries.

How to build a LaunchDarkly integration in Martini

Objective

Establish authenticated access to LaunchDarkly and protect credentials according to the workflow’s required permissions.

Instructions in Martini

  • Use a dedicated least-privilege LaunchDarkly service token for production workflows.
  • Store the token as a secured Martini environment value.
  • Use OAuth 2.0 only where delegated application access is required.
  • Keep SDK keys and client-side IDs separate from REST API credentials.

Objective

Select an event-driven, API-driven, or scheduled trigger based on the required timeliness and completeness.

Instructions in Martini

  • Use a Martini API or supported webhook endpoint for inbound requests and selected LaunchDarkly notifications.
  • Use a scheduler for inventory reconciliation and missed-event detection.
  • Use a controlled API request for release or administrative actions.

Objective

Obtain the current LaunchDarkly resource or query projection before transforming or mutating it.

Instructions in Martini

  • Consume the versioned REST API for administrative resources and mutations.
  • Use GraphQL for supported read and nested-data projections.
  • Follow pagination and avoid assuming the first response is complete.
  • Retrieve current flag state before sensitive mutations.

Objective

Coordinate validation, enrichment, approvals, downstream calls, and state recording in a maintainable Martini workflow.

Instructions in Martini

  • Separate webhook receipt from downstream business processing when delivery reliability matters.
  • Apply project, environment, permission, and production-change rules.
  • Persist operation identifiers for asynchronous bulk actions.

Objective

Convert LaunchDarkly objects and events into canonical and target-specific schemas without losing important targeting semantics.

Instructions in Martini

  • Map projects, environments, flags, segments, contexts, and members using stable identifiers.
  • Preserve context kinds, attributes, variations, and targeting rule order.
  • Retain useful unknown fields when storing payloads for audit or replay.

Objective

Apply authorized changes or synchronize normalized data to the target system with accurate status reporting.

Instructions in Martini

  • Use upsert behavior for inventory synchronization.
  • Call downstream notification, change, incident, or observability APIs only after validation.
  • Do not report asynchronous bulk work as complete until individual results are evaluated.

Common LaunchDarkly data objects used in integrations

ObjectTypical UseCommon target systemsMartini handling
ProjectsOrganize feature flags and related configuration.ServiceNow, Jira, governance databases, reporting platformsMartini retrieves projects through REST or supported GraphQL queries, maps stable identifiers and metadata, and upserts them during inventory synchronization.
EnvironmentsRepresent development, staging, production, and other deployment contexts within a project.Release platforms, ServiceNow, deployment pipelinesMartini validates project and environment combinations before mutations and synchronizes environment metadata to downstream systems.
Feature flagsControl application behavior, releases, targeting, variations, and progressive delivery.GitHub, Azure DevOps, ServiceNow, Slack, DatadogMartini retrieves current state before changes, applies approval and business rules, maps targeting data, and performs authorized REST mutations with idempotency controls.
SegmentsDefine reusable groups of users or contexts for targeting rules.Governance catalogs, reporting databases, release systemsMartini retrieves and maps segment definitions while preserving identifiers and targeting relationships for inventory or governance workflows.
ContextsRepresent users, devices, organizations, or other identifiable entities evaluated by targeting rules.Release governance stores, analytics and operational platformsMartini preserves context kinds, attributes, segments, variations, and rule order rather than assuming a user-only model.
MembersRepresent LaunchDarkly account users and their role-based access to projects and environments.Identity governance, audit stores, administrative reportingMartini can synchronize member and permission metadata for review, while keeping production mutation workflows restricted to least-privilege service tokens.

Authentication and security considerations

Use least-privilege credentials

LaunchDarkly REST requests use an access token. A dedicated service token is generally preferable for production Martini workflows to a personal access token, with permissions limited to the required projects, environments, and actions.

Protect credentials and access

Store LaunchDarkly credentials as secured Martini environment values. Keep REST access tokens separate from SDK keys and client-side IDs, which serve different application-runtime purposes.

Control production mutations

  • Authenticate and authorize callers of any Martini API façade.
  • Validate project, environment, flag, variation, and approval data before mutations.
  • Separate read-only inventory workflows from production write workflows.
  • Record administrative requests and resulting LaunchDarkly state for audit.

Operational considerations for LaunchDarkly integrations

Rate limits and pagination

Handle HTTP 429 responses, respect Retry-After when supplied, use bounded exponential backoff, and avoid unnecessary polling. Treat collection endpoints as paginated and continue until no additional results remain.

Idempotency and concurrency

Use stable resource identifiers, persisted webhook delivery IDs where available, upserts, and request journals. Retrieve current flag state before mutations when concurrent user, pipeline, or integration changes could otherwise be overwritten.

Webhooks and asynchronous actions

LaunchDarkly webhook coverage is selective. Validate incoming requests, persist events when necessary, suppress duplicates, and use scheduled reconciliation to detect missed changes. Bulk operations may be asynchronous and require status polling plus per-item failure handling.

Schema and lifecycle management

Pin the applicable API version, tolerate optional fields, test changes in non-production environments, and monitor API changes. Preserve context targeting semantics and distinguish active, temporary, stale, and ownerless feature flags before applying lifecycle policies.

Why use Martini instead of scripts or point-to-point integrations?

Centralize integration logic

Martini provides a maintainable workflow layer between LaunchDarkly and release, incident, observability, collaboration, and governance systems. It keeps authentication, validation, mapping, approvals, and downstream behavior in controlled integration assets rather than scattered scripts.

Support multiple interaction styles

Martini can consume REST and supported GraphQL APIs, receive selected webhook notifications, expose controlled APIs, and run scheduled reconciliation workflows. This supports both near-real-time notifications and completeness-oriented synchronization.

Improve reliability and control

  • Apply reusable transformations and business rules.
  • Handle pagination, rate limits, retries, duplicate deliveries, and asynchronous bulk operations.
  • Protect production flag mutations with authorization and approval checks.
  • Monitor workflow execution and preserve actionable error context.

Frequently asked questions

How can LaunchDarkly be integrated with enterprise systems?

LaunchDarkly can be integrated through its versioned REST API, selected GraphQL queries, supported webhook and integration notifications, bulk operations, and SDK-based application services. REST is the main mechanism for administrative resources and feature-flag changes; GraphQL is useful for supported read projections, while webhooks provide selective change notifications.

Can Martini integrate with LaunchDarkly?

Yes. Martini can consume LaunchDarkly REST and GraphQL APIs, receive supported LaunchDarkly webhook events, expose controlled APIs for internal release workflows, and orchestrate synchronization, approvals, transformations, retries, and downstream notifications.

Do I need a connector to integrate LaunchDarkly with Martini?

No. A dedicated LaunchDarkly connector is not required. Martini can integrate using LaunchDarkly’s confirmed native REST and GraphQL APIs, supported webhook events, authentication methods, and other documented endpoints.

Is there any extra Lonti cost to integrate LaunchDarkly with Martini?

Lonti does not charge an additional per-connector or per-vendor fee to integrate LaunchDarkly. Integrations are subject to the provisioned capacity of the Martini environment. Separate costs may apply from LaunchDarkly, infrastructure providers, or other third-party systems depending on subscription, usage, and deployment model.

Which LaunchDarkly integration methods should an enterprise use?

Use the REST API for projects, environments, feature flags, segments, members, audit data, and administrative mutations. Use GraphQL for supported read and nested-data queries, webhooks for selected change notifications, and bulk operations where their narrower asynchronous scope is appropriate.

Can Martini receive LaunchDarkly webhook events?

Yes, Martini can expose an API endpoint or webhook-consuming workflow for supported LaunchDarkly notifications. Coverage is selective rather than universal, so the integration should validate the configured event scope, account for incomplete payloads and duplicate deliveries, and use scheduled reconciliation when completeness is important.

How should LaunchDarkly data synchronization handle mapping and errors?

Use stable LaunchDarkly identifiers, paginated retrieval, upserts, and canonical mappings for projects, environments, flags, segments, contexts, and members. Martini workflows can apply validation and business rules, handle rate limits and 429 responses with bounded backoff, and record failures for retry or operator review.

Can Martini expose an API façade for LaunchDarkly?

Yes. Martini can expose a controlled API that hides direct LaunchDarkly administrative access behind authentication, authorization, validation, approvals, business rules, and audit handling. The façade can call LaunchDarkly REST APIs while returning a target-specific contract to internal systems.

Related Martini documentation

APIs

Webhooks

Security

Transformation

Operations

Connect LaunchDarkly with your enterprise systems

Use Martini to build governed LaunchDarkly integrations for feature-flag administration, release coordination, inventory synchronization, and selected change notifications.

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