Ellipse Gradient for Header

xAI API Integration Guide

Connect enterprise applications to xAI models through Bearer-authenticated REST APIs for generation, embeddings, images, model discovery, and supported batch workloads.

xAI API integration options at a glance

The xAI API provides HTTP REST endpoints for model discovery, chat completions, unified responses, embeddings, image generation, and supported batch workloads. Requests use an xAI API key in the HTTP Authorization Bearer header. No official GraphQL or SOAP API, general-purpose webhook mechanism, or customer database interface was confirmed. Martini can consume the REST endpoints, construct and validate JSON payloads, map source data into prompts or structured requests, and persist or return validated responses. For supported asynchronous workloads, Martini can coordinate batch submission, status retrieval, and result reconciliation through scheduled workflows.

Integration pointSupported by xAI API?Common use casesHow Martini supports it
REST APIsYesAccess models, create chat completions and responses, generate embeddings or images, and submit or retrieve supported batch work.Martini can consume xAI HTTP endpoints, construct JSON request bodies, map responses, expose a controlled API, and orchestrate downstream writes.
OpenAI-compatible API structureYesReuse familiar request conventions for supported xAI functionality and simplify multi-provider application designs.Martini can call the underlying xAI endpoints directly; mappings must still be validated against xAI-specific models, limits, fields, and response schemas.
Bulk / async / batch APIsYesProcess supported groups of independent requests when immediate responses are not required.Martini can submit batches, schedule status retrieval, reconcile results using stable source identifiers, and handle retryable failures.
AuthenticationYesAuthenticate API requests with an xAI API key supplied as a Bearer token in the HTTP Authorization header.Martini can keep the API key in secrets or protected environment configuration and apply it to outbound REST requests.
Webhooks / outbound callbacksNot confirmedNo general-purpose webhook or callback mechanism was confirmed for standard completions, responses, embeddings, or image generation.Martini should use synchronous REST calls or scheduled polling for documented asynchronous operations rather than assume callback delivery.
GraphQL APIsNot confirmedNo official xAI GraphQL API was verified in the supplied research.Martini supports GraphQL consumption generally, but this xAI integration should use the confirmed REST interface instead.
SOAP APIsNoNo official xAI SOAP API was confirmed.Martini should not design this integration around SOAP; it can consume xAI REST APIs instead.
File / attachment APIsNot confirmedFile input may be relevant to selected features or batch workflows, but a generally available file or attachment API was not confirmed.Martini can extract or transform content before calling an xAI operation that accepts the resulting text or structured data, subject to endpoint validation.
Database / analytics accessNoxAI provides model APIs rather than direct database or analytics-query access to customer application data.Martini can obtain source data from an enterprise database or application, send the required subset to xAI, and persist validated results in a target system.

How xAI API exposes data and business events

xAI API REST APIs

The xAI API exposes REST operations for model discovery, chat completions, responses, embeddings, image generation, and supported batch operations. Requests use JSON payloads and an API key in the Authorization Bearer header. No general-purpose webhook delivery was confirmed for ordinary requests.

Martini implementation pattern

Martini implementation pattern: a workflow receives an application request or retrieves source data, builds the xAI JSON payload, calls the selected REST endpoint, validates the response against the expected model-specific structure, and returns or persists the result. Secrets, correlation identifiers, timeouts, and retry rules are managed outside the business payload.

Implementation sequence

Receive an application request or source record
Load the approved xAI API key and model configuration
Map source fields into the xAI JSON request
Call the selected xAI REST endpoint
Validate the response and required business fields
Write the result to the target system or return it through a Martini API

xAI API Batch API

xAI documents batch-style processing for supported workloads where independent requests can be processed asynchronously. Exact endpoint coverage, input format, limits, retention, and result retrieval behavior must be checked against the current xAI documentation.

Martini implementation pattern

Martini implementation pattern: a scheduled or manually initiated workflow prepares bounded requests with stable source identifiers, submits a batch, records its status, and retrieves or polls results according to the documented xAI behavior. A reconciliation workflow validates outputs and separates retryable, failed, and completed items.

Implementation sequence

Select eligible source items for batch processing
Assign a stable identifier to each request
Map items into the documented batch input format
Submit the batch to the xAI API
Store the batch identifier and submission status
Retrieve or poll results according to the current API contract

xAI API Authentication

xAI API access uses API keys supplied in the HTTP Authorization header as Bearer tokens. OAuth 2.0, separately issued JWTs, and configurable API-key scopes were not confirmed for xAI API access.

Martini implementation pattern

Martini implementation pattern: the API key is stored as a Martini secret or protected environment value and injected into the outbound REST request at runtime. Workflows avoid placing credentials in prompts, mappings, source control, logs, or returned business data.

Implementation sequence

Create or obtain the xAI API key in the xAI developer console
Store the key in Martini secrets or protected environment configuration
Configure the outbound request to use Bearer authentication
Restrict access to the workflow and secret
Redact credentials and sensitive payloads from logs

Common xAI API integration patterns

Pattern 1: Summarize support cases

When to use this pattern

Use this pattern when support teams need consistent summaries, issue categories, urgency indicators, or suggested internal responses without replacing human review. It is suitable for new or updated cases where the source conversation can be filtered and bounded before processing.

Integration direction
Support platform
Martini
xAI API
Support platform
Example Mapping
xAI API FieldCanonical FieldTarget Field
case.descriptionsourceTextmessages.content
case.conversationHistoryconversationContextmessages
case.idcorrelationIdworkflow.correlationId
generated.summarycaseSummarycase.internalSummary
Martini implementation pattern

Martini retrieves the case, removes unnecessary personal data, truncates or summarizes oversized history, and calls a chat-completion or response endpoint. It validates the category, urgency, and summary before updating the case, preserves human-authored content, and applies bounded retries for transient failures.

Martini capabilities used
  • workflows
  • API consumption
  • data mapping
  • business rules
  • response validation
  • error handling

Pattern 2: Enrich CRM leads and opportunities

When to use this pattern

Use this pattern when sales teams need structured qualification notes, account summaries, or next-action recommendations generated from approved CRM fields. Generated values should be treated as suggestions and validated before updating business objects.

Integration direction
Salesforce
Martini
xAI API
Salesforce
Example Mapping
xAI API FieldCanonical FieldTarget Field
Lead.companyaccountNamequalification.accountName
Lead.descriptionbusinessContextmessages.content
Opportunity.stageNamesalesStagequalification.stage
generated.nextActionrecommendedActionOpportunity.nextAction
Martini implementation pattern

A Martini workflow receives a Lead or Opportunity, combines approved business context, sends a structured request to xAI, and validates enumerations, identifiers, and text length. Business rules determine whether the result is written automatically, routed for approval, or recorded as an exception; correlation IDs prevent duplicate updates after retries.

Martini capabilities used
  • API consumption
  • workflows
  • data mapping
  • structured response validation
  • business rules
  • idempotency

Pattern 3: Classify and extract document content

When to use this pattern

Use this pattern when a document platform or extraction service has produced text and the business needs document type, supplier name, effective date, contract identifier, or other structured fields. The selected xAI endpoint must support the required content format.

Integration direction
Document system
Martini
xAI API
Document system
Example Mapping
xAI API FieldCanonical FieldTarget Field
document.extractedTextdocumentTextmessages.content
document.iddocumentIdmetadata.sourceId
generated.documentTypedocumentTypedocument.classification
generated.effectiveDateeffectiveDatedocument.effectiveDate
Martini implementation pattern

Martini receives document metadata and extracted text, checks size and content rules, and submits a structured classification or extraction request. It validates dates, identifiers, and enumerated values before writing the result to the repository or database; malformed responses go to review rather than being silently persisted.

Martini capabilities used
  • workflows
  • JSON handling
  • data mapping
  • validation
  • business rules
  • error handling

Pattern 4: Generate embeddings for search enrichment

When to use this pattern

Use this pattern when product descriptions, knowledge articles, or internal documents must be indexed for semantic search. It is appropriate for scheduled or change-driven enrichment where source versions can be tracked and reprocessed safely.

Integration direction
Source system
Martini
xAI API
Vector store
Example Mapping
xAI API FieldCanonical FieldTarget Field
article.bodycontentembedding.input
article.idsourceIdvector.metadata.sourceId
article.versioncontentVersionvector.metadata.version
embedding.vectorembeddingvector.values
Martini implementation pattern

Martini retrieves changed content, computes or stores a stable content version, sends bounded text to the xAI embeddings endpoint, and writes the returned vector with source metadata. The workflow skips unchanged content, retries transient failures with backoff, and reconciles results so duplicate vectors are not created.

Martini capabilities used
  • scheduled workflows
  • API consumption
  • data mapping
  • database connectivity
  • idempotency
  • retry handling

Applications commonly integrated with xAI API

The following are typical enterprise architecture patterns rather than xAI-certified integrations. Martini can place validation, data governance, prompt construction, response handling, and target-system updates around the xAI API.

Application Scenario Direction Martini Pattern
Salesforce Summarize Leads, Accounts, Opportunities, or Cases and write validated enrichment back to CRM objects. Salesforce → Martini → xAI API → Salesforce A Martini workflow receives or retrieves the Salesforce object, removes unnecessary sensitive data, maps approved fields into a structured xAI request, validates the response, and updates Salesforce only when the result meets the workflow rules.
ServiceNow Classify incidents, summarize work notes, suggest assignment categories, or extract request details. ServiceNow → Martini → xAI API → ServiceNow Martini consumes the relevant ServiceNow data, builds a controlled prompt or structured response request, validates classifications and summaries, and writes approved results back with correlation and retry handling.
Zendesk Summarize tickets, classify support intent, and generate internal response suggestions. Zendesk → Martini → xAI API → Zendesk A workflow retrieves ticket content, truncates or summarizes oversized histories, calls the xAI REST API, validates generated fields, and stores the result as a controlled ticket update or review item.
Jira Summarize issues, classify defects, and generate structured release or sprint notes. Jira → Martini → xAI API → Jira Martini maps Jira issue fields and approved comments into an xAI request, applies output validation and business rules, and writes summaries or classifications back without overwriting human-authored content.
Slack Provide an internal assistant, summarize selected conversations, or route approved prompts through a controlled API. Slack → Martini → xAI API → Slack Martini exposes a controlled REST API for approved Slack interactions, authenticates and authorizes the caller, sends selected content to xAI, and returns or posts only validated output.
Microsoft Teams Summarize approved messages or expose an internal AI workflow through a controlled application endpoint. Microsoft Teams → Martini → xAI API → Microsoft Teams A Martini API accepts an approved Teams request, applies data filtering and prompt rules, calls xAI through a secured workflow, and returns the response with observability and error handling.
Snowflake Enrich or classify governed datasets and write generated results to warehouse tables. Snowflake → Martini → xAI API → Snowflake Martini reads an approved dataset subset, sends bounded text workloads to xAI or its batch API, associates each result with a stable source identifier, validates output, and writes enrichment results back to Snowflake.
NetSuite Enrich customer, item, or transaction-related text with classifications or summaries for operational workflows. NetSuite → Martini → xAI API → NetSuite Martini retrieves selected NetSuite fields, maps them into a structured xAI request, validates the generated classification or summary, and updates NetSuite subject to business approval rules.

How to build a xAI API integration in Martini

Objective

Establish the outbound xAI API request with the correct endpoint, Bearer authentication, environment-specific model configuration, and protected credentials.

Instructions in Martini

  • Create or obtain an xAI API key through the xAI developer console.
  • Store the key in Martini secrets or protected environment configuration.
  • Configure the REST request to send the key in the Authorization Bearer header.
  • Keep model selection and endpoint settings environment-specific where required.

Objective

Select the execution model that matches the workload, such as an application request, source-system change, scheduled enrichment run, or batch operation.

Instructions in Martini

  • Use an API-driven workflow for synchronous generation requests.
  • Use a source-system trigger or controlled application endpoint when immediate processing is required.
  • Use a scheduler for periodic enrichment, model discovery, or batch-status retrieval.
  • Do not assume xAI webhook callbacks are available for ordinary requests.

Objective

Collect only the business data needed for the xAI operation and prepare it for a bounded, governed request.

Instructions in Martini

  • Retrieve the source object, document text, conversation, or approved dataset subset.
  • Remove secrets and unnecessary personal or confidential information.
  • Truncate, summarize, or partition oversized input where appropriate.
  • Assign a stable correlation or source identifier.

Objective

Coordinate the xAI request, response handling, conditional routing, and downstream persistence as a maintainable Martini workflow.

Instructions in Martini

  • Build the JSON request from workflow input and configuration.
  • Call the selected xAI REST endpoint through Martini.
  • Separate transport errors, rate limits, validation failures, and authentication failures.
  • Route asynchronous batch work through submission, status, retrieval, and reconciliation stages.

Objective

Transform xAI responses into a canonical internal structure and verify that generated values are safe for downstream use.

Instructions in Martini

  • Map response fields into the canonical integration model.
  • Validate required fields, enumerations, dates, identifiers, and numeric values.
  • Record model and content-version metadata where relevant.
  • Route invalid or low-confidence results to review instead of silently updating systems.

Objective

Control how generated content is used, including approval requirements, overwrite protection, data governance, and target-specific conditions.

Instructions in Martini

  • Prevent generated content from overwriting human-authored fields without an explicit rule.
  • Require approval for customer-facing or financially significant outputs when appropriate.
  • Apply source-specific privacy and retention rules.
  • Skip unchanged content and prevent duplicate processing using correlation identifiers.

Common xAI API data objects used in integrations

ObjectTypical UseCommon target systemsMartini handling
ModelsDiscover available Grok models and their metadata before selecting a model for a workflow.Configuration stores, application services, monitoring storesMartini can retrieve and validate model metadata, cache an approved selection, and keep model choice in environment configuration rather than hard-coding it in mappings.
Chat CompletionsGenerate conversational text, summaries, classifications, suggested responses, or structured business notes from message-based prompts.Salesforce, ServiceNow, Zendesk, Jira, Slack, Microsoft TeamsMartini maps approved source content into messages, applies payload and privacy rules, validates the response, and routes accepted output to the target workflow.
ResponsesSubmit unified response-generation requests for supported xAI models and modalities.Internal APIs, business applications, review queuesMartini constructs the request from workflow input, validates model-specific fields, records correlation metadata, and returns or persists the normalized result.
EmbeddingsCreate vector representations for product descriptions, knowledge articles, documents, or other text.Vector stores, search services, PostgreSQL, SnowflakeMartini sends bounded text, associates each vector with a source identifier and content version, and writes results to the selected vector-capable store.
Image GenerationsGenerate images using supported Grok image models for approved application workflows.Content repositories, review systems, internal applicationsMartini submits validated generation requests, handles the returned response according to the current API contract, and routes results through approval or storage workflows.
BatchesProcess supported groups of independent requests asynchronously when immediate responses are unnecessary.Data warehouses, enrichment tables, operational databases, reporting storesMartini submits batches, stores a stable source identifier for each request, polls or retrieves results as documented, and reconciles successes and failures idempotently.

Authentication and security considerations

Bearer API-key authentication

xAI API requests use an API key in the HTTP Authorization header as a Bearer token. OAuth 2.0, separately issued JWTs, and configurable API-key scopes were not confirmed for xAI API access.

Secret handling

Store the xAI API key in Martini secrets or protected environment configuration. Do not embed it in workflow payloads, mappings, source control, prompts, or application logs.

Data governance

  • Send only the source data required for the selected operation.
  • Remove credentials and unnecessary personal or confidential information from prompts.
  • Restrict access to workflows and secrets according to environment policy.
  • Redact sensitive prompts and generated content from operational logs.

Operational considerations for xAI API integrations

Limits, cost, and payload size

Confirm request, token, concurrency, batch, and model-specific limits in the xAI account and current API documentation. Prompt size and generated output can affect latency and usage cost, so apply bounded input and controlled concurrency.

Retries and idempotency

Use appropriate timeouts for generation requests and bounded backoff for transient errors and rate limits. Do not retry authentication failures, malformed requests, or validation errors. Persist correlation identifiers and response metadata where available to prevent duplicate updates.

Models and schemas

Model names, capabilities, context limits, response formats, and deprecation schedules can change. Keep mappings version-controlled, validate model availability, and avoid depending on undocumented response fields.

Testing and observability

  • Test prompts, structured response contracts, model changes, and failure paths before deployment.
  • Log correlation IDs, endpoint names, model identifiers, status codes, latency, and retry counts.
  • Do not log API keys, sensitive prompts, or unredacted generated content.
  • Reconcile batch results against stable source identifiers and retain failed items for controlled reprocessing.

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

Centralized orchestration

Martini places xAI API calls inside governed workflows rather than scattering HTTP requests across scripts and application code. The same workflow can retrieve source data, call xAI, apply business rules, validate output, and update multiple targets.

Reusable integration assets

Teams can expose a controlled Martini API that standardizes prompts, model selection, authentication, response handling, and error behavior for consuming applications. This creates a reusable boundary without exposing xAI credentials to every caller.

Maintainable data processing

Martini provides explicit mapping, transformation, validation, scheduling, retry, and monitoring capabilities. These controls are useful when generated content must be governed, reviewed, reconciled, and safely written to enterprise systems.

Frequently asked questions

How can xAI API be integrated with enterprise systems?

The xAI API can be integrated through its HTTP REST endpoints using an API key in the Authorization Bearer header. Enterprise workflows can call model discovery, chat completion, response, embedding, image-generation, and supported batch operations, then validate and write results to business applications or data stores.

Can Martini integrate with xAI API?

Yes. Martini can integrate with xAI API by consuming its native REST endpoints, securely supplying the xAI API key, mapping enterprise data into JSON requests, validating responses, and orchestrating downstream updates or API responses.

Do I need a connector to integrate xAI API with Martini?

No dedicated xAI API connector is required. Martini can use xAI API's confirmed REST and Bearer-authentication mechanisms directly, with workflows handling request construction, transformation, validation, retries, and persistence.

Is there any extra Lonti cost to integrate xAI API with Martini?

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

Which xAI API integration methods should new implementations use?

REST APIs are the primary method. Use the documented endpoints for models, chat completions, responses, embeddings, image generation, and supported batch workloads. OpenAI-compatible request conventions may help with payload design, but xAI-specific models, fields, limits, and response schemas must still be verified.

Does xAI API provide webhooks or callbacks for completed requests?

A general-purpose webhook or outbound callback mechanism for ordinary completions, responses, embeddings, or image-generation requests was not confirmed. Synchronous REST requests are the normal pattern, while supported asynchronous work should use the documented batch process and result retrieval behavior.

How should synchronization and data mapping work with xAI API?

Martini can retrieve source data, map it into a canonical prompt or structured request, call xAI, validate the response, and write approved results to a target system. Stable source identifiers, content versions, and correlation IDs support idempotent enrichment and batch reconciliation.

How should errors, retries, and generated responses be handled?

Martini should distinguish authentication failures, malformed requests, rate limits, transient server errors, and business validation failures. Bounded retries with backoff are appropriate for retryable conditions, while generated fields should be validated and routed for review when they are incomplete, invalid, or unsuitable for automatic updates.

Related Martini documentation

REST APIs

Workflows

Data Processing

Security

Operations

Build governed xAI API integrations with Martini

Use Martini to connect enterprise data and applications to xAI API through secure REST workflows, reusable APIs, controlled transformations, and operational safeguards.

EXPLORE MARTINI
BOOK A DEMO