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https://console.groq.com llms.txt

For agents: Groq's llms.txt indexes developer docs for integrating fast LLM inference with partner tools such as JigsawStack and Parallel for web search and AI research age

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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-24T03:39:40.860Z. Cached copy · Source on groq.com

# https://console.groq.com llms.txt

- [JigsawStack 🧩](https://console.groq.com/docs/jigsawstack): The JigsawStack 🧩 documentation page provides an overview of the AI SDK, detailing its capabilities in automating tasks such as web scraping, OCR, and translation using Large Language Models (LLMs), as well as its features like the Prompt Engine and built-in safety guardrails. This page serves as a starting point for integrating JigsawStack into existing applications and learning about its key features and setup process.
- [Groq API Reference](https://console.groq.com/docs/api-reference): The Groq API Reference provides detailed information on the available APIs, endpoints, and parameters for interacting with Groq's services, enabling developers to integrate and utilize Groq's capabilities in their applications. This reference guide serves as a comprehensive resource for understanding and using the Groq API to build, deploy, and manage AI-driven solutions.
- [Parallel + Groq: Fast Web Search for Real-Time AI Research](https://console.groq.com/docs/parallel): This documentation page provides a guide on integrating Parallel's real-time web search capabilities with Groq's high-speed inference to build AI research agents that can quickly find and analyze current information. It offers a step-by-step quick start guide, code examples, and advanced use cases for applications such as multi-company comparisons, real-time market data retrieval, and breaking news monitoring.
- [Security Onboarding](https://console.groq.com/docs/production-readiness/security-onboarding): The "Security Onboarding" guide provides best practices and recommendations for securing API keys, client configurations, and integrations when using Groq's API, ensuring a secure and reliable experience. This page outlines shared security responsibilities between Groq and customers, covering key management, transport security, input safety, and incident response.
- [Production-Ready Checklist for Applications on GroqCloud](https://console.groq.com/docs/production-readiness/production-ready-checklist): The "Production-Ready Checklist for Applications on GroqCloud" provides a comprehensive guide for deploying and scaling LLM applications on GroqCloud, covering critical aspects such as model selection, performance optimization, monitoring, and cost management. This checklist helps developers ensure their Groq-powered applications are launched and scaled with confidence, minimizing common pitfalls and optimizing user experience, operational costs, and system reliability.
- [Understanding and Optimizing Latency on Groq](https://console.groq.com/docs/production-readiness/optimizing-latency): This guide provides a comprehensive overview of latency in Groq-powered applications, helping developers understand, measure, and optimize latency for production deployment. It covers key metrics, factors affecting latency, and optimization strategies for building efficient Large Language Model (LLM) applicati
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