{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"groq-api","slug":"groq-api","name":"Groq API","type":"api","url":"https://console.groq.com","page_url":"https://unfragile.ai/groq-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"groq-api__cap_0","uri":"capability://text.generation.language.openai.compatible.ultra.fast.text.generation.with.lpu.acceleration","name":"openai-compatible ultra-fast text generation with lpu acceleration","description":"Generates text using Groq's custom LPU (Language Processing Unit) hardware, which achieves 500+ tokens/second throughput by parallelizing token computation across specialized silicon. Implements OpenAI API compatibility layer, allowing drop-in replacement via custom baseURL parameter without SDK changes. Supports models including GPT-OSS-120B, GPT-OSS-20B, Llama-4-Scout, Llama-3.3-70B, and Qwen-3-32B with streaming and batch processing tiers.","intents":["I need to generate text faster than cloud LLM providers for latency-sensitive applications","I want to migrate from OpenAI API without rewriting client code","I need to handle high-throughput inference at scale without queuing delays","I want to reduce per-token inference costs by using specialized hardware"],"best_for":["developers building real-time chat applications requiring sub-100ms response times","teams migrating from OpenAI with existing OpenAI SDK integrations","builders of high-volume inference pipelines processing 1000+ requests/minute","startups optimizing LLM inference costs for production workloads"],"limitations":["Context window specifications not publicly documented — maximum input/output token limits unknown","Model selection limited to Groq's curated set; cannot fine-tune or deploy custom models","Latency claims (500+ tokens/sec, lowest latency) are marketing statements without independent benchmarks provided","OpenAI compatibility is request/response format only — advanced features like vision may have different schemas"],"requires":["API key from Groq console (free tier available)","OpenAI SDK (Python 0.28.0+ or Node.js 18+) or direct HTTP client","Network connectivity to https://api.groq.com/openai/v1","Understanding of Bearer token authentication"],"input_types":["text (string)","structured prompts with system/user/assistant roles"],"output_types":["text (string)","structured JSON (via function calling)","streaming token chunks (if streaming enabled)"],"categories":["text-generation-language","llm-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_1","uri":"capability://tool.use.integration.function.calling.and.tool.use.with.schema.based.routing","name":"function calling and tool use with schema-based routing","description":"Enables models (GPT-OSS-120B, GPT-OSS-20B, Llama-4-Scout, Qwen-3-32B) to invoke external tools by generating structured function calls based on a provided schema. Works by embedding tool definitions in the system prompt or via function parameter arrays, allowing the model to decide when and how to call tools. Integrates with built-in tools (Web Search, Browser Automation, Code Execution, Wolfram Alpha) and supports remote tools via MCP (Model Context Protocol) connectors.","intents":["I need my LLM to decide when to search the web vs. use local knowledge","I want to build an agent that can execute code and see results in real-time","I need to connect my LLM to Google Workspace (Gmail, Calendar, Drive) for productivity tasks","I want to define custom tools and have the model route requests to them automatically"],"best_for":["developers building autonomous agents with multi-step reasoning","teams integrating LLMs with enterprise tools (Google Workspace, Slack, etc.)","builders creating code-execution sandboxes where LLMs can test hypotheses","non-technical founders prototyping AI assistants without custom backend logic"],"limitations":["Tool definitions must be provided in OpenAI function-calling format; custom schema formats not supported","Built-in tools (Web Search, Code Execution) have undocumented rate limits and execution timeouts","MCP connector support is mentioned but implementation details and available connectors are not documented","No built-in error recovery — if a tool call fails, the model must be prompted to retry","Google Workspace connectors require OAuth setup and are only 'now available' (feature maturity unknown)"],"requires":["API key for Groq","Tool schema definitions in OpenAI function-calling format (JSON Schema)","For built-in tools: no additional setup required","For MCP tools: MCP server running and accessible to Groq API (architecture unknown)","For Google Workspace: OAuth credentials and workspace domain configuration"],"input_types":["text prompt","function schema array (JSON Schema format)","conversation history with tool call results"],"output_types":["function call objects with name and arguments","text response after tool execution","structured data from tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_10","uri":"capability://tool.use.integration.browser.automation.and.code.execution.for.agent.workflows","name":"browser automation and code execution for agent workflows","description":"Enables models to automate browser interactions (clicking, typing, navigation) and execute code in a sandboxed environment. Available as built-in tools that can be invoked via function calling. Browser Automation allows the model to interact with web pages as if a human were using them. Code Execution allows the model to run Python or JavaScript code and see results. Both tools integrate into the same function-calling system as Web Search.","intents":["I need my agent to fill out web forms or navigate websites automatically","I want to let the LLM execute code to test hypotheses or solve problems","I need to build a system that can interact with web applications without APIs","I want to create an agent that can debug code by running it and analyzing errors"],"best_for":["developers building autonomous agents for web automation (RPA, testing)","teams creating code-generation systems that need to verify generated code","builders of debugging tools that execute code to find issues","researchers exploring agent capabilities with code execution"],"limitations":["Browser automation capabilities (supported actions, page load timeouts) are not documented","Code execution sandbox restrictions and available libraries are unknown","Maximum execution time per code block is not specified","Supported programming languages for code execution are not documented (Python, JavaScript, or both?)","No information on file system access, network access, or other sandbox constraints","Error handling and timeout behavior are not documented","No pricing information for tool execution vs. text generation"],"requires":["API key for Groq","Model that supports tool use (GPT-OSS-120B, GPT-OSS-20B, Llama-4-Scout, Qwen-3-32B)","Function calling enabled with Browser Automation or Code Execution tools in schema"],"input_types":["text prompt (triggering browser automation or code execution)","code snippet (for code execution tool)","URL or page state (for browser automation)"],"output_types":["browser interaction results (page content, screenshots, or action confirmation)","code execution results (stdout, stderr, return values)","text response incorporating tool results"],"categories":["tool-use-integration","automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_11","uri":"capability://tool.use.integration.google.workspace.integration.for.productivity.automation","name":"google workspace integration for productivity automation","description":"Provides native connectors for Google Workspace services (Gmail, Google Calendar, Google Drive) that can be invoked via function calling. Models can read/write emails, manage calendar events, and access files without requiring custom OAuth implementation. Connectors are described as 'now available,' suggesting recent addition. Exact API surface (read-only vs. write, supported operations) is not documented.","intents":["I need my agent to read and respond to emails automatically","I want to build a scheduling assistant that can create calendar events","I need to create a document management system that accesses Google Drive files","I want to automate productivity workflows (email triage, meeting scheduling) with LLMs"],"best_for":["enterprise teams automating email and calendar workflows","developers building AI assistants for knowledge workers","teams creating productivity tools that integrate with Google Workspace","organizations with Google Workspace as their primary productivity platform"],"limitations":["Supported operations per service are not documented (read-only? write? delete?)","OAuth setup and permission scoping requirements are not detailed","Rate limits for Workspace API calls are not specified","Unclear whether connectors support all Google Workspace features or only basic operations","No information on data retention or privacy handling for Workspace data","Feature is marked 'now available,' suggesting potential stability or maturity concerns","No documentation on error handling for failed Workspace operations"],"requires":["API key for Groq","Google Workspace account with OAuth credentials","Model that supports tool use (GPT-OSS-120B, GPT-OSS-20B, Llama-4-Scout, Qwen-3-32B)","Function calling enabled with Workspace connectors in schema"],"input_types":["text prompt (triggering Workspace operations)","email addresses, calendar event details, file paths"],"output_types":["email content, calendar events, file metadata","confirmation of write operations","text response incorporating Workspace data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_12","uri":"capability://automation.workflow.flexible.processing.tier.for.variable.workload.optimization","name":"flexible processing tier for variable workload optimization","description":"Offers a 'Flex Processing' service tier alongside real-time and batch tiers, allowing users to optimize for different workload patterns. Exact characteristics of Flex Processing (latency SLA, pricing, use cases) are not documented. Mentioned as available tier in documentation but implementation details are absent.","intents":["I need a middle ground between real-time and batch processing for semi-urgent workloads","I want to optimize costs by choosing different processing tiers for different requests","I need variable latency guarantees depending on workload priority"],"best_for":["teams with mixed workload patterns (some urgent, some non-urgent)","applications that can tolerate variable latency in exchange for cost savings","organizations optimizing for cost-per-inference across different use cases"],"limitations":["Flex Processing characteristics are completely undocumented","Latency SLA, throughput guarantees, and pricing are unknown","Unclear how to select Flex Processing tier or configure it","No information on when to use Flex vs. real-time vs. batch","Feature may be in beta or experimental stage"],"requires":["API key for Groq","Flex Processing tier access (may require paid account)"],"input_types":["text prompt"],"output_types":["text response"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_13","uri":"capability://text.generation.language.free.tier.access.with.rate.limited.inference","name":"free tier access with rate-limited inference","description":"Provides free access to Groq API with rate limits and quota restrictions, allowing developers to experiment and build prototypes without payment. Free tier includes access to multiple models and all core features (text generation, function calling, etc.). Exact rate limits, quota sizes, and feature restrictions are not documented.","intents":["I want to try Groq API without committing to paid tier","I need to build a prototype or MVP with minimal upfront costs","I want to evaluate Groq's performance before scaling to production","I need a free tier for educational or research projects"],"best_for":["individual developers and hobbyists","startups prototyping MVP features","students and researchers evaluating LLM APIs","teams evaluating Groq before committing to paid plans"],"limitations":["Rate limits per minute/hour are not specified","Monthly quota or token limits are not documented","Unclear which models are available on free tier (all or subset?)","No information on feature restrictions (e.g., batch processing, caching availability)","Unclear if free tier has different latency SLAs or priority","No documentation on upgrade path from free to paid tier"],"requires":["Groq account (free signup at https://console.groq.com)","API key generation from console"],"input_types":["text prompt"],"output_types":["text response"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_14","uri":"capability://automation.workflow.free.tier.api.access.with.usage.based.billing.and.spend.limits","name":"free tier api access with usage-based billing and spend limits","description":"Offers free tier with monthly token allowance for experimentation and development, transitioning to pay-as-you-go pricing for production use. Developers can set spend limits to prevent unexpected charges. Billing is per-token (input and output tokens priced separately). Projects and API key management enable cost allocation across teams and applications.","intents":["I want to try Groq API without credit card for prototyping and testing","I need to control costs by setting spending limits on API keys","I want to allocate API costs across multiple projects and teams"],"best_for":["individual developers and students prototyping LLM applications","teams evaluating Groq before committing to production spend","enterprises managing costs across multiple projects and API keys"],"limitations":["Free tier limits not documented — unclear monthly token allowance or rate limits","Pricing per token not documented — cannot compare cost vs OpenAI, Anthropic, or other providers","Spend limit enforcement mechanism not documented — unclear if hard limit (requests rejected) or soft limit (alerts)","Billing cycle and invoice details not documented","No documented volume discounts or enterprise pricing","Project-level cost tracking not documented — unclear if cost attribution is automatic or manual"],"requires":["Groq console account (free signup at https://console.groq.com)","API key generation from console","Optional: credit card for paid tier","Optional: spend limit configuration"],"input_types":["API usage (token counts from requests)","spend limit configuration (optional, in console)"],"output_types":["usage dashboard (tokens used, cost)","billing invoice (monthly)","spend alerts (if limit approaching)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_15","uri":"capability://automation.workflow.batch.processing.and.asynchronous.inference.for.cost.optimization","name":"batch processing and asynchronous inference for cost optimization","description":"Provides batch processing mode for non-real-time inference workloads, accepting multiple requests in bulk and processing them asynchronously with lower per-token cost than real-time API. Batch jobs are queued and processed during off-peak hours, trading latency for cost savings. Results are returned via webhook or polling. Ideal for large-scale data processing, content generation, and analysis tasks.","intents":["I need to process thousands of documents or queries cost-effectively without real-time latency requirements","I want to generate large volumes of content (summaries, descriptions, translations) with lower per-token cost","I need to analyze datasets using LLM inference without paying premium real-time pricing"],"best_for":["developers processing large document collections or datasets with LLMs","teams generating bulk content (product descriptions, summaries, translations)","builders optimizing cost for non-latency-sensitive workloads"],"limitations":["Batch processing feature mentioned in documentation but implementation details not provided","Cost savings percentage not documented — unclear how much cheaper batch is vs real-time","Processing time SLA not documented — unclear if hours, days, or weeks for batch completion","Batch size limits not documented","Webhook vs polling mechanism not documented","Error handling and retry policy for failed batch jobs not documented","No documented support for streaming or partial results"],"requires":["API key from Groq console","Batch request format (JSON Lines or similar, format unknown)","Webhook endpoint for result delivery OR polling mechanism","Batch size within limits (limits unknown)"],"input_types":["batch request file (multiple prompts, format TBD)","webhook URL for result delivery (optional)","batch configuration (timeout, retry policy)"],"output_types":["batch job ID","batch status (queued, processing, completed)","results file (format TBD)","cost savings metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_2","uri":"capability://image.visual.multimodal.inference.with.vision.and.speech.to.text","name":"multimodal inference with vision and speech-to-text","description":"Processes images and audio inputs alongside text using specialized models: Llama-4-Scout for vision tasks and Whisper-Large-v3 (or Turbo variant) for speech-to-text transcription. Vision model accepts images in unspecified formats and returns structured analysis or text descriptions. Whisper models transcribe audio to text with language detection. Both modalities integrate into the same `/responses` endpoint as text generation, allowing multimodal reasoning chains.","intents":["I need to extract text or analyze content from images without building a separate vision pipeline","I want to transcribe audio files and then reason over the transcribed text","I need to build a chatbot that accepts images, audio, and text in a single request","I want to process documents (PDFs, screenshots) and extract structured data"],"best_for":["developers building document processing pipelines (invoices, receipts, forms)","teams creating voice-enabled chatbots or voice-to-action workflows","builders of accessibility tools that convert images/audio to text","product teams adding multimodal capabilities to existing text-only LLM applications"],"limitations":["Vision input formats not documented — unclear if base64, URLs, or file uploads are supported","Image size limits, resolution requirements, and supported formats (JPEG, PNG, WebP, etc.) are undocumented","Whisper-Large-v3-Turbo is mentioned but performance/accuracy tradeoffs vs. standard v3 are not detailed","Audio input formats, sample rates, and maximum duration limits are not specified","No OCR-specific capability documented despite 'OCR/Image Recognition' mentioned in architecture analysis"],"requires":["API key for Groq","Image files (format and size limits unknown) for vision tasks","Audio files (format and duration limits unknown) for speech-to-text","Understanding of how to encode images/audio in request payload (encoding method not documented)"],"input_types":["image (format unspecified)","audio (format unspecified)","text (for context or follow-up questions)"],"output_types":["text (transcription from Whisper, analysis from vision model)","structured data (if function calling is combined with vision)","confidence scores or metadata (if returned by models)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_3","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content moderation and safety filtering","description":"Uses Safety-GPT-OSS-20B model to classify and filter potentially harmful content (hate speech, violence, sexual content, etc.). Operates as a separate model endpoint that can be called before or after generation to validate prompts or outputs. Returns safety classification scores or filtered text depending on configuration. Integrates into the same `/responses` endpoint as other models.","intents":["I need to filter user-generated prompts before sending them to my main LLM","I want to validate LLM outputs for harmful content before showing them to users","I need to log and track safety violations for compliance auditing","I want to implement content policies without building custom classifiers"],"best_for":["teams building consumer-facing LLM applications with content moderation requirements","platforms handling user-generated content that must comply with community standards","enterprises with regulatory requirements (COPPA, GDPR, industry-specific policies)","developers adding safety guardrails to existing LLM systems"],"limitations":["Safety model capabilities and classification categories are not documented","No information on false positive/negative rates or accuracy benchmarks","Unclear whether Safety-GPT-OSS-20B can be used for real-time filtering or only batch analysis","No documentation on how to configure sensitivity levels or custom safety policies","Integration with other Groq models (e.g., chaining safety check + generation) is not detailed"],"requires":["API key for Groq","Text input to classify (prompt or generated output)","Understanding of expected safety categories (not documented)"],"input_types":["text (user prompt or LLM output)"],"output_types":["safety classification (categories and scores unknown)","filtered text (if filtering is enabled)","boolean flag (safe/unsafe)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_4","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.inference","name":"reasoning and chain-of-thought inference","description":"Enables extended reasoning capabilities on models supporting reasoning tasks (GPT-OSS-120B, GPT-OSS-20B, Qwen-3-32B). Models can generate intermediate reasoning steps before producing final answers, improving accuracy on complex problems. Reasoning is triggered via prompt engineering or dedicated reasoning parameters (if supported). Works within the same `/responses` endpoint and respects the same token limits as standard generation.","intents":["I need my LLM to show its work for complex math or logic problems","I want to improve accuracy on multi-step reasoning tasks by forcing intermediate steps","I need to debug why an LLM is making incorrect decisions by seeing its reasoning","I want to build a system that can verify reasoning steps independently"],"best_for":["developers building educational tools or tutoring systems","teams working on complex reasoning tasks (math, logic, code review)","builders creating explainable AI systems where reasoning transparency is required","researchers evaluating LLM reasoning capabilities"],"limitations":["Reasoning mechanism (prompt engineering vs. native parameter) is not documented","No specification of reasoning token overhead or impact on latency","Unclear which models support reasoning and which do not (only 3 models listed as supporting reasoning)","No documentation on how to extract or validate intermediate reasoning steps","Reasoning output format and structure are not specified"],"requires":["API key for Groq","Model that supports reasoning (GPT-OSS-120B, GPT-OSS-20B, or Qwen-3-32B)","Prompt engineering to trigger reasoning (or native parameter if available)"],"input_types":["text prompt (with reasoning trigger)"],"output_types":["text with intermediate reasoning steps","final answer after reasoning","structured reasoning trace (format unknown)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_5","uri":"capability://automation.workflow.batch.processing.and.asynchronous.inference","name":"batch processing and asynchronous inference","description":"Supports batch processing tier for non-real-time inference workloads, allowing multiple requests to be submitted together and processed asynchronously. Reduces per-request costs compared to real-time inference by amortizing overhead across batches. Exact batch size limits, processing time SLAs, and submission/retrieval mechanisms are not documented. Mentioned as 'Batch Processing' service tier in documentation.","intents":["I need to process thousands of documents overnight without paying real-time inference prices","I want to reduce costs for non-urgent LLM tasks by batching requests","I need to submit a large dataset for analysis and retrieve results asynchronously","I want to implement a job queue system for LLM inference"],"best_for":["data teams processing large datasets with LLMs (content classification, summarization, extraction)","startups optimizing LLM costs by deferring non-critical inference","teams building ETL pipelines that can tolerate latency (minutes to hours)","researchers running large-scale LLM experiments"],"limitations":["Batch processing API specification is completely undocumented — no endpoint, request format, or response schema provided","Batch size limits, maximum requests per batch, and processing time SLAs are unknown","No information on how to retrieve batch results or check status","Unclear whether batch processing supports all models or only a subset","No pricing information for batch tier vs. real-time tier","Error handling and retry logic for failed batch jobs are not documented"],"requires":["API key for Groq","Batch processing tier access (may require paid account)","Batch submission API (specification unknown)","Mechanism to retrieve results (unknown)"],"input_types":["batch of text prompts (format unknown)","batch metadata (structure unknown)"],"output_types":["batch job ID (format unknown)","batch results (format unknown)","status/progress information (format unknown)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_6","uri":"capability://memory.knowledge.prompt.caching.for.repeated.inference.patterns","name":"prompt caching for repeated inference patterns","description":"Caches prompt prefixes (system prompts, context, examples) to avoid reprocessing identical input sequences across multiple requests. When the same prefix is used in subsequent requests, the cached tokens are reused, reducing latency and token consumption. Mechanism and configuration details are not documented, but caching is listed as a documented feature. Works within the same `/responses` endpoint.","intents":["I want to reduce latency and costs when running multiple queries with the same system prompt","I need to cache large context windows (documents, code repositories) for repeated analysis","I want to implement few-shot prompting efficiently without re-encoding examples each time","I need to optimize inference for multi-turn conversations with consistent system context"],"best_for":["developers building chatbots with consistent system prompts across many conversations","teams analyzing multiple documents with the same analysis template","builders of code-analysis tools that reuse large codebases as context","applications with few-shot prompting patterns (e.g., classification with examples)"],"limitations":["Prompt caching configuration and API parameters are completely undocumented","Cache invalidation strategy and TTL (time-to-live) are unknown","Unclear whether caching is automatic or requires explicit configuration","No information on cache hit rates, memory limits, or cost savings from caching","Cache key generation logic (how prefixes are matched) is not specified","Unclear if caching works across different users/API keys or only within a single session"],"requires":["API key for Groq","Repeated requests with identical prompt prefixes","Understanding of caching configuration (if manual setup required)"],"input_types":["text prompt with cacheable prefix"],"output_types":["text response (same as non-cached requests)","cache metadata (if returned)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_7","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Constrains model outputs to match a provided JSON schema, ensuring generated text conforms to a specific structure (e.g., extracting fields into a JSON object). Works by embedding schema constraints into the generation process, preventing the model from producing invalid JSON. Exact implementation (grammar-based constraints, post-generation validation, or native model support) is not documented. Listed as a documented feature but details are absent.","intents":["I need to extract structured data (entities, relationships) from unstructured text","I want to generate JSON API responses directly from an LLM without post-processing","I need to ensure LLM outputs can be parsed by downstream systems without error handling","I want to build a form-filling system where the LLM generates valid JSON for each field"],"best_for":["developers building data extraction pipelines (invoices, forms, contracts)","teams integrating LLMs into structured APIs (REST endpoints returning JSON)","builders of no-code/low-code platforms where LLM outputs must be predictable","data teams automating ETL with LLM-generated structured data"],"limitations":["Schema format and validation mechanism are completely undocumented","Unclear whether schemas use JSON Schema, TypeScript interfaces, or custom format","No information on schema complexity limits or performance impact","Unknown how validation errors are handled (retry, fallback, error response)","Unclear if nested schemas, conditional fields, or complex types are supported","No documentation on schema versioning or evolution"],"requires":["API key for Groq","JSON schema definition (format unknown)","Model that supports structured outputs (unclear which models support this)"],"input_types":["text prompt","JSON schema definition"],"output_types":["JSON object matching schema","validation error (if schema validation fails)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_8","uri":"capability://text.generation.language.text.to.speech.synthesis.with.multilingual.support","name":"text-to-speech synthesis with multilingual support","description":"Converts text to natural-sounding speech using Orpheus models (Orpheus-English, Orpheus-Arabic-Saudi). Models are accessed via the same `/responses` endpoint as text generation. Output is audio in unspecified format. Supports at least English and Arabic (Saudi dialect), with language selection via model parameter. Voice characteristics and audio quality settings are not documented.","intents":["I need to generate audio narration for content without using external TTS services","I want to build a voice-enabled chatbot that speaks responses to users","I need to create multilingual audio content (English and Arabic) from text","I want to add accessibility features (text-to-speech) to my application"],"best_for":["developers building voice-enabled chatbots or voice assistants","teams creating accessible applications for users with visual impairments","content creators generating audio narration for videos or podcasts","multilingual applications serving Arabic-speaking users"],"limitations":["Only two languages documented: English and Arabic (Saudi dialect) — no support for other languages or dialects","Audio output format, sample rate, and bitrate are not specified","No information on voice selection, gender, or accent customization","Audio quality settings (speed, pitch, emotion) are not documented","Maximum text length per request is unknown","Unclear whether streaming audio output is supported","No pricing information for TTS vs. text generation"],"requires":["API key for Groq","Text input to synthesize","Model selection (orpheus-english or orpheus-arabic-saudi)"],"input_types":["text (language-specific)"],"output_types":["audio (format unspecified)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__cap_9","uri":"capability://search.retrieval.web.search.integration.for.real.time.information.retrieval","name":"web search integration for real-time information retrieval","description":"Enables models to search the web and incorporate current information into responses. Web Search is available as a built-in tool that can be invoked via function calling. When triggered, the model queries the web and receives search results, which it can then use to answer user questions. Exact search provider, result format, and integration mechanism are not documented. Supported on GPT-OSS models and Llama-4-Scout.","intents":["I need my chatbot to answer questions about current events or recent news","I want to build a research assistant that can look up information in real-time","I need to augment my LLM with web search without building a custom search integration","I want to verify facts by searching the web before generating responses"],"best_for":["developers building chatbots that need current information (news, weather, stock prices)","teams creating research or fact-checking tools","builders of customer support systems that need to look up product information","applications where hallucination risk is high and web verification is needed"],"limitations":["Search provider is not specified (Google, Bing, DuckDuckGo, or proprietary)","Search result format and metadata (URLs, snippets, rankings) are not documented","No information on search rate limits or number of results per query","Unclear how search results are ranked or filtered before being passed to the model","No documentation on search query generation (how the model decides what to search for)","Latency impact of web search on overall response time is unknown","No information on handling search failures or no-results scenarios"],"requires":["API key for Groq","Model that supports Web Search (GPT-OSS-120B, GPT-OSS-20B, Llama-4-Scout)","Function calling enabled with Web Search tool in schema"],"input_types":["text prompt (triggering web search)"],"output_types":["text response incorporating web search results","search results (format unknown)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"groq-api__headline","uri":"capability://text.generation.language.ultra.fast.llm.inference.api","name":"ultra-fast llm inference api","description":"The Groq API is an ultra-fast LLM inference API that leverages custom hardware to deliver industry-leading low latency and high throughput for text generation and processing tasks.","intents":["best LLM inference API","LLM API for low latency applications","fast text generation API","high-performance LLM API for developers","OpenAI-compatible LLM API"],"best_for":["developers needing high-speed LLM processing","applications requiring low-latency responses"],"limitations":[],"requires":["API key for access"],"input_types":["text prompts"],"output_types":["generated text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key from Groq console (free tier available)","OpenAI SDK (Python 0.28.0+ or Node.js 18+) or direct HTTP client","Network connectivity to https://api.groq.com/openai/v1","Understanding of Bearer token authentication","API key for Groq","Tool schema definitions in OpenAI function-calling format (JSON Schema)","For built-in tools: no additional setup required","For MCP tools: MCP server running and accessible to Groq API (architecture unknown)","For Google Workspace: OAuth credentials and workspace domain configuration","Model that supports tool use (GPT-OSS-120B, GPT-OSS-20B, Llama-4-Scout, Qwen-3-32B)"],"failure_modes":["Context window specifications not publicly documented — maximum input/output token limits unknown","Model selection limited to Groq's curated set; 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