{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"xai-grok-api","slug":"xai-grok-api","name":"xAI Grok API","type":"api","url":"https://console.x.ai","page_url":"https://unfragile.ai/xai-grok-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"xai-grok-api__cap_0","uri":"capability://text.generation.language.real.time.x.twitter.data.augmented.text.generation","name":"real-time x (twitter) data-augmented text generation","description":"Grok-2 model with live access to X platform data, enabling generation of responses grounded in current events, trending topics, and real-time social discourse. The model integrates X data retrieval at inference time rather than relying on static training data cutoffs, allowing it to reference events happening within hours or minutes of the API call. Requests include optional context parameters to specify time windows, trending topics, or specific accounts to prioritize in the knowledge context.","intents":["Generate summaries of breaking news or trending topics with current context","Create responses to real-time events that require knowledge of what's happening now","Build chatbots that can discuss current events without hallucinating outdated information","Develop applications that need to cite or reference recent social media discourse"],"best_for":["News aggregation platforms and real-time content creators","Social media analytics tools requiring current event context","Chatbot developers building conversational AI for time-sensitive domains","Teams building AI agents that need to stay informed about breaking news"],"limitations":["Real-time data retrieval adds latency (typically 2-5 seconds per request) compared to static-context models","X data availability depends on API rate limits and platform uptime; cannot guarantee coverage of all trending topics","Context window for X data integration is limited; cannot retrieve full historical timelines for extended periods","Requires active X API credentials and may incur additional costs for high-volume data retrieval"],"requires":["Valid API key from xAI console","X (Twitter) API access or xAI's integrated X data pipeline credentials","HTTP/2 capable client for optimal performance with streaming responses"],"input_types":["text (natural language prompts)","structured parameters (time_window, trending_topics, account_filters)"],"output_types":["text (generated response with real-time context)","structured metadata (source citations, timestamps, confidence scores)"],"categories":["text-generation-language","real-time-data-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_1","uri":"capability://image.visual.multimodal.vision.language.generation.with.grok.vision","name":"multimodal vision-language generation with grok-vision","description":"Grok-Vision processes images alongside text prompts to generate descriptions, answer visual questions, extract structured data from images, and perform visual reasoning tasks. The model uses a vision encoder to convert images into embeddings that are fused with text embeddings in a unified transformer architecture, enabling joint reasoning over both modalities. Supports batch processing of multiple images per request and returns structured outputs including bounding boxes, object labels, and confidence scores.","intents":["Analyze screenshots, diagrams, or charts and extract key information or insights","Answer questions about image content without manual annotation","Extract structured data (tables, forms, text) from images for downstream processing","Generate alt-text or captions for images in accessibility or content management workflows"],"best_for":["Document processing and data extraction pipelines","Accessibility tools generating alt-text for images","Visual search and image understanding applications","Content moderation systems requiring visual context analysis"],"limitations":["Image resolution is capped at 2048x2048 pixels; larger images are automatically downsampled, potentially losing fine-grained details","Batch processing limited to 10 images per request; applications requiring bulk image analysis must implement pagination","Vision performance degrades on highly stylized, artistic, or synthetic images compared to photorealistic content","No built-in image generation capability; vision is read-only (analysis only, not creation)"],"requires":["Valid API key from xAI console","Images in JPEG, PNG, WebP, or GIF format","Base64 encoding or URL-accessible image URIs","Maximum image file size of 20MB per image"],"input_types":["image (JPEG, PNG, WebP, GIF)","text (natural language questions or prompts about the image)"],"output_types":["text (descriptions, answers, extracted information)","structured data (JSON with bounding boxes, object labels, confidence scores)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_2","uri":"capability://tool.use.integration.openai.compatible.api.endpoint.abstraction","name":"openai-compatible api endpoint abstraction","description":"Grok API implements the OpenAI API specification (chat completions, embeddings, streaming) as a drop-in replacement, allowing developers to swap Grok models into existing OpenAI-based codebases with minimal changes. The implementation maps Grok model identifiers (grok-2, grok-vision) to OpenAI's message format, supporting the same request/response schemas, streaming protocols, and error handling patterns. This compatibility layer abstracts away Grok-specific features (like X data integration) as optional parameters while maintaining full backward compatibility with standard OpenAI client libraries.","intents":["Migrate existing OpenAI-based applications to Grok without rewriting client code","Implement multi-model fallback logic that treats Grok as an alternative provider","Use existing OpenAI SDKs (Python, Node.js, Go) without installing Grok-specific libraries","A/B test Grok against OpenAI models in production with minimal code changes"],"best_for":["Teams with existing OpenAI integrations looking to evaluate Grok","Multi-provider LLM applications requiring provider abstraction","Developers building LLM applications who want to avoid vendor lock-in","Organizations migrating from OpenAI to reduce costs or improve performance"],"limitations":["Not all OpenAI-specific parameters are supported; custom Grok parameters (e.g., x_data_context) may conflict with OpenAI SDK validation","Streaming response format matches OpenAI's SSE protocol but Grok-specific metadata fields may not be recognized by older OpenAI client versions","Function calling (tools) support is compatible with OpenAI schema but Grok's tool execution semantics may differ in edge cases","Rate limiting and quota management differ between OpenAI and xAI; existing rate-limit handling code may need adjustment"],"requires":["OpenAI Python SDK (v1.0+) or Node.js SDK (v4.0+) or compatible HTTP client","Valid xAI API key (obtained from console.x.ai)","Endpoint URL override to https://api.x.ai/v1 instead of OpenAI's endpoint","Model identifier updated to grok-2 or grok-vision instead of gpt-4"],"input_types":["text (chat messages in OpenAI format)","structured data (function/tool definitions in OpenAI JSON schema)"],"output_types":["text (chat completions in OpenAI format)","structured data (function calls, token usage metadata)"],"categories":["tool-use-integration","api-compatibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_3","uri":"capability://text.generation.language.streaming.response.generation.with.server.sent.events.sse","name":"streaming response generation with server-sent events (sse)","description":"Grok API supports streaming text generation via HTTP Server-Sent Events (SSE), allowing clients to receive tokens incrementally as they are generated rather than waiting for the full response. The implementation uses chunked transfer encoding with JSON-formatted delta objects, compatible with OpenAI's streaming format. Clients can process tokens in real-time, enabling low-latency UI updates, early stopping, and progressive rendering of long-form content. Streaming is compatible with both text-only and multimodal requests.","intents":["Build chat interfaces with real-time token streaming for responsive user experience","Implement progressive rendering of long-form content (articles, code) as it's generated","Create cost-aware applications that can stop generation early if sufficient tokens have been received","Develop interactive debugging tools that show model reasoning step-by-step"],"best_for":["Web applications and chat interfaces requiring low-latency user feedback","Content generation tools where progressive rendering improves perceived performance","Cost-sensitive applications that benefit from early stopping","Real-time collaborative tools (e.g., pair programming, co-writing)"],"limitations":["Streaming adds ~100-200ms overhead per chunk due to SSE framing and network round-trips; not suitable for ultra-low-latency applications","Client must handle connection drops and implement reconnection logic; no built-in retry mechanism in the API","Streaming responses cannot be cached as easily as full responses; each stream is unique and time-bound","Token counting is approximate during streaming; final token usage is only available after the stream completes"],"requires":["HTTP client with SSE support (native fetch API, axios, httpx, etc.)","Valid xAI API key","stream=true parameter in request body","Ability to parse JSON-formatted SSE events"],"input_types":["text (chat messages)","structured parameters (stream=true, max_tokens, temperature)"],"output_types":["streaming text (SSE events with delta tokens)","structured metadata (finish_reason, usage stats after stream ends)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_4","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.invocation","name":"function calling with schema-based tool invocation","description":"Grok API supports structured function calling via OpenAI-compatible tool definitions, allowing the model to invoke external functions by returning structured JSON with function names and arguments. The implementation uses JSON schema to define tool signatures, and the model learns to call tools when appropriate based on the task. The API returns tool_calls in the response, which the client must execute and feed back to the model via tool_result messages. This enables agentic workflows where the model can decompose tasks into function calls, handle errors, and iterate.","intents":["Build AI agents that can call APIs, databases, or custom functions to accomplish tasks","Create workflows where the model decides which tools to use based on user intent","Implement multi-step reasoning where the model calls tools, observes results, and adjusts its approach","Develop applications that integrate LLMs with external systems (CRMs, analytics, payment processors)"],"best_for":["AI agent frameworks and autonomous task automation","Enterprise applications integrating LLMs with existing APIs and databases","Developers building multi-step workflows with tool composition","Teams implementing ReAct or similar agentic reasoning patterns"],"limitations":["Tool calling adds latency (typically 500ms-2s per tool invocation) due to model reasoning and client-side tool execution","Model may hallucinate tool calls or use incorrect argument types; client must validate tool calls before execution","No built-in tool execution sandboxing; client is responsible for security (input validation, rate limiting, error handling)","Tool definitions are limited to JSON schema; complex or dynamic tool signatures require workarounds"],"requires":["Valid xAI API key","Tool definitions in OpenAI-compatible JSON schema format","Client-side tool execution logic (the API does not execute tools)","Ability to handle multi-turn conversations with tool_result messages"],"input_types":["text (user prompts)","structured data (tool definitions in JSON schema, previous tool results)"],"output_types":["structured data (tool_calls with function names and arguments)","text (model reasoning or final response after tool execution)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_5","uri":"capability://data.processing.analysis.token.level.usage.tracking.and.cost.estimation","name":"token-level usage tracking and cost estimation","description":"Grok API returns detailed token usage information (prompt_tokens, completion_tokens, total_tokens) in every response, enabling developers to track costs and implement token budgets. The API uses a transparent pricing model where costs are calculated as (prompt_tokens * prompt_price + completion_tokens * completion_price). Clients can estimate costs before making requests by calculating token counts locally using the same tokenizer as the API, or by using the API's token counting endpoint. Usage data is aggregated in the xAI console for billing and analytics.","intents":["Implement cost controls and token budgets in LLM applications","Estimate API costs before making requests to optimize spending","Track per-user or per-feature token consumption for chargeback or analytics","Optimize prompts and model selection based on token efficiency metrics"],"best_for":["Cost-conscious startups and teams with limited API budgets","SaaS applications that need to track per-user LLM costs","Developers optimizing prompt engineering for token efficiency","Organizations implementing LLM cost allocation and chargeback systems"],"limitations":["Token counting is approximate for local estimation; actual token counts may differ by 1-2% due to tokenizer variations","Usage data is reported with a delay (typically 5-15 minutes) in the xAI console; real-time cost tracking requires client-side aggregation","No built-in rate limiting or quota enforcement; applications must implement their own token budgets","Pricing may change without notice; clients must periodically refresh pricing information from the API"],"requires":["Valid xAI API key with billing information","Access to xAI console for viewing usage and billing data","Optional: local tokenizer library (if pre-request cost estimation is needed)"],"input_types":["text (prompts for token counting)","structured data (usage queries, billing period filters)"],"output_types":["structured data (token counts, cost estimates, usage aggregations)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_6","uri":"capability://memory.knowledge.context.window.management.with.message.history.truncation","name":"context window management with message history truncation","description":"Grok API manages context windows (the maximum number of tokens the model can process in a single request) by accepting a messages array where each message contributes to the total token count. The API enforces a maximum context window (typically 128K tokens for Grok-2) and returns an error if the total exceeds the limit. Developers can implement automatic message truncation strategies (e.g., keep the most recent N messages, summarize old messages, or drop low-priority messages) to fit within the context window. The API provides token counts for each message to enable precise truncation.","intents":["Maintain long-running conversations without losing context by selectively truncating message history","Implement smart context management that prioritizes recent or important messages","Build chatbots that can handle multi-turn conversations spanning hours or days","Optimize token usage by removing redundant or low-value messages from the history"],"best_for":["Chatbot and conversational AI applications with long-running sessions","Customer support systems that need to maintain context across multiple interactions","Collaborative tools (pair programming, co-writing) where context accumulates over time","Applications with memory constraints or cost sensitivity"],"limitations":["Truncating message history loses information; the model cannot reference truncated messages, potentially leading to repeated questions or context loss","No built-in summarization; developers must implement their own summarization logic or use external summarization APIs","Context window size is fixed per model; cannot dynamically increase context for important conversations","Truncation strategies are application-specific; no one-size-fits-all solution provided by the API"],"requires":["Valid xAI API key","Message history stored in application (not persisted by the API)","Logic to calculate token counts and implement truncation strategies","Ability to handle context window exceeded errors and retry with truncated history"],"input_types":["structured data (messages array with role, content, and optional token counts)"],"output_types":["text (model response)","structured data (token usage for each message and total)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_7","uri":"capability://automation.workflow.rate.limiting.and.quota.management.with.per.minute.and.per.day.caps","name":"rate limiting and quota management with per-minute and per-day caps","description":"Grok API enforces rate limits on a per-API-key basis, with separate limits for requests-per-minute (RPM) and tokens-per-minute (TPM). The API returns HTTP 429 (Too Many Requests) responses when limits are exceeded, along with Retry-After headers indicating when the client can retry. Developers can query their current usage and limits via the API or xAI console. Rate limits vary by plan (free tier, paid tiers, enterprise) and can be increased by contacting xAI support. The API does not provide built-in queuing or backoff logic; clients must implement their own retry strategies.","intents":["Implement exponential backoff and retry logic to handle rate limit errors gracefully","Monitor API usage and alert when approaching rate limits","Distribute requests across multiple API keys or accounts to increase throughput","Plan capacity and upgrade API plans based on usage trends"],"best_for":["Production applications requiring reliable error handling and retry logic","High-throughput systems that need to manage API quotas carefully","Teams monitoring API usage and planning capacity upgrades","Multi-tenant applications that need to allocate quotas per customer"],"limitations":["Rate limits are per-API-key, not per-user; applications must implement their own per-user quotas if needed","No built-in queuing or priority system; all requests are treated equally","Rate limit headers are returned only on 429 responses; clients cannot proactively check remaining quota","Limits may be reset at unpredictable times (e.g., when upgrading plans); clients must handle limit changes gracefully"],"requires":["Valid xAI API key","HTTP client capable of reading response headers (Retry-After)","Retry logic with exponential backoff (recommended: 1s, 2s, 4s, 8s, 16s)","Monitoring and alerting infrastructure to track usage trends"],"input_types":["structured data (rate limit queries, usage reports)"],"output_types":["structured data (current usage, limits, Retry-After headers)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_8","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimized.asynchronous.requests","name":"batch processing api for cost-optimized asynchronous requests","description":"Grok API offers a batch processing endpoint that accepts multiple requests in a single JSONL file, processes them asynchronously, and returns results in bulk. Batch requests are charged at a 50% discount compared to real-time API calls, making them suitable for non-urgent, high-volume workloads. Clients submit a batch file, receive a batch ID, and poll for completion status. Results are returned as a JSONL file that can be downloaded or streamed. Batch processing typically completes within 1-24 hours depending on queue depth.","intents":["Process large volumes of text (e.g., summarizing thousands of documents) at reduced cost","Generate embeddings for a large corpus of documents for semantic search","Perform bulk content moderation or classification tasks","Run nightly or weekly batch jobs that don't require real-time responses"],"best_for":["Cost-sensitive applications processing large volumes of data","Batch data processing pipelines (ETL, data enrichment)","Organizations with non-urgent, high-volume LLM workloads","Teams building search indexes or knowledge bases that require periodic updates"],"limitations":["Batch processing adds 1-24 hour latency; not suitable for real-time or interactive applications","Batch files are limited to 100MB or 100,000 requests; larger workloads must be split into multiple batches","No real-time X data context available in batch mode; batches use static training data only","Batch results are not guaranteed to be in the same order as input requests; clients must track request IDs"],"requires":["Valid xAI API key","Batch file in JSONL format (one request per line)","Ability to poll for batch status or implement webhook notifications","Storage for batch results (local file, S3, database, etc.)"],"input_types":["structured data (JSONL file with request objects)"],"output_types":["structured data (JSONL file with response objects)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__cap_9","uri":"capability://safety.moderation.error.handling.with.detailed.error.codes.and.recovery.suggestions","name":"error handling with detailed error codes and recovery suggestions","description":"Grok API returns structured error responses with HTTP status codes, error codes (e.g., 'invalid_request_error', 'rate_limit_error', 'server_error'), human-readable error messages, and optional recovery suggestions. Common errors include invalid API keys (401), malformed requests (400), rate limit exceeded (429), and server errors (5xx). The API provides Retry-After headers for rate limit and server errors, enabling clients to implement intelligent retry logic. Error responses include a unique request ID for debugging and support inquiries.","intents":["Implement robust error handling and user-friendly error messages","Debug API integration issues by analyzing error codes and messages","Implement automatic retry logic with appropriate backoff strategies","Monitor API health and alert on persistent errors"],"best_for":["Production applications requiring reliable error handling","Teams debugging API integration issues","Monitoring and observability systems tracking API health","Customer-facing applications that need to display helpful error messages"],"limitations":["Error messages are generic and may not provide enough context for complex issues; developers may need to contact support","Some errors (e.g., 'invalid_request_error') can have multiple root causes; clients must parse the error message to determine the specific issue","Retry-After headers are only provided for certain error types (429, 5xx); clients must implement their own backoff for other errors","Error codes are not versioned; changes to error codes or messages may break client-side error handling logic"],"requires":["HTTP client capable of reading response status codes and headers","Error handling logic to parse and respond to different error types","Logging and monitoring infrastructure to track errors"],"input_types":["structured data (error responses with status codes and error codes)"],"output_types":["structured data (error details, recovery suggestions, request IDs)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"xai-grok-api__headline","uri":"capability://llm.apis.ai.powered.real.time.data.api","name":"ai-powered real-time data api","description":"An API that provides real-time access to X (Twitter) data for current events, featuring Grok-2 for text generation and Grok-Vision for multimodal capabilities, all in an OpenAI-compatible format.","intents":["best AI data API","real-time data API for current events","Grok API for text generation","multimodal API for images and text","OpenAI-compatible API for developers"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Valid API key from xAI console","X (Twitter) API access or xAI's integrated X data pipeline credentials","HTTP/2 capable client for optimal performance with streaming responses","Images in JPEG, PNG, WebP, or GIF format","Base64 encoding or URL-accessible image URIs","Maximum image file size of 20MB per image","OpenAI Python SDK (v1.0+) or Node.js SDK (v4.0+) or compatible HTTP client","Valid xAI API key (obtained from console.x.ai)","Endpoint URL override to https://api.x.ai/v1 instead of OpenAI's endpoint","Model identifier updated to grok-2 or grok-vision instead of gpt-4"],"failure_modes":["Real-time data retrieval adds latency (typically 2-5 seconds per request) compared to static-context models","X data availability depends on API rate limits and platform uptime; cannot guarantee coverage of all trending topics","Context window for X data integration is limited; cannot retrieve full historical timelines for extended periods","Requires active X API credentials and may incur additional costs for high-volume data retrieval","Image resolution is capped at 2048x2048 pixels; larger images are automatically downsampled, potentially losing fine-grained details","Batch processing limited to 10 images per request; applications requiring bulk image analysis must implement pagination","Vision performance degrades on highly stylized, artistic, or synthetic images compared to photorealistic content","No built-in image generation capability; vision is read-only (analysis only, not creation)","Not all OpenAI-specific parameters are supported; custom Grok parameters (e.g., x_data_context) may conflict with OpenAI SDK validation","Streaming response format matches OpenAI's SSE protocol but Grok-specific metadata fields may not be recognized by older OpenAI client versions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.804Z","last_scraped_at":null,"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=xai-grok-api","compare_url":"https://unfragile.ai/compare?artifact=xai-grok-api"}},"signature":"2i1Prod+j00azwBi9b/E3Ny7xXFu0BviJYy3upfonlLhpLyAlUtY1Q66XIn7z7A005onTekNZU4EGAtv7i3wBw==","signedAt":"2026-06-22T09:21:04.184Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xai-grok-api","artifact":"https://unfragile.ai/xai-grok-api","verify":"https://unfragile.ai/api/v1/verify?slug=xai-grok-api","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"}}