Skill UI

This guide identifies and avoids anti-patterns and common integration mistakes when working with Exa's neural search API. It covers crucial topics such as distinguishing between keyword and neural searches, properly retrieving content, handling date filters, and managing client instances. Use this when reviewing Exa-powered code, onboarding new team members, or auditing existing integrations for robustness and correctness.

A comprehensive guide and toolkit for performing load testing, capacity planning, and implementing scaling strategies for Exa API integrations. It covers best practices for managing rate limits (QPS), using tools like k6, implementing caching (LRU), and optimizing search architectures for high-throughput, low-latency applications, such as RAG systems.

This guide provides a comprehensive setup for a fast and reproducible local development workflow tailored for Exa API integrations. It covers establishing optimal project structure, implementing detailed unit tests using mock responses, and setting up robust integration tests. Ideal for developers who need to rapidly build, test, and iterate on applications relying on Exa's search and content fetching capabilities.

A comprehensive guide for migrating search pipelines from traditional keyword-based APIs (Google, Bing, Tavily) to Exa's advanced neural search capabilities. This guide covers building an adapter layer, implementing feature-flag traffic shifting, translating legacy query syntax, and leveraging Exa's unique features like semantic and similarity search for superior results.

This guide details how to configure and manage the Exa search API across development, staging, and production environments. It ensures API key isolation, implements environment-specific search limits, and provides strategies for cost control and performance optimization by utilizing caching layers like Redis. Essential for scalable, multi-stage deployments.

A comprehensive guide and code implementation for setting up observability, metrics, and alerting for Exa search integrations. This skill tracks crucial KPIs such as search latency (P50/P95), error rates, result usage, and cache hit ratios. It is essential for monitoring search quality, debugging performance bottlenecks, and managing API resource usage in production environments.

This guide provides advanced technical patterns for optimizing Exa API performance in production workloads. It covers critical strategies such as selecting the optimal search type based on latency budgets (e.g., instant vs. neural), implementing robust result caching using LRU, minimizing content retrieval size, and executing parallel queries to maximize throughput. Essential for building reliable, low-latency, and cost-effective search integrations.

A comprehensive guardrail system for external search APIs (like Exa). It enforces content safety policies, manages domain access via allow/blocklists, sanitizes queries to prevent misuse, implements result freshness checks, and enforces per-user usage quotas. Essential for building reliable, secure, and cost-controlled integrations.

A comprehensive guide and implementation pattern for handling external API rate limits (HTTP 429 errors) and service throttling. It demonstrates advanced techniques like exponential backoff with jitter, request queuing using concurrency control, and adaptive rate limiting, ensuring stable and reliable API interaction even under high load or strict usage quotas.

This skill provides the production-ready reference architecture for integrating Exa into complex applications. It covers advanced search pipelines, RAG implementation for context generation, and structured content discovery. Use this to design new Exa integrations, review project structures, or establish industry-standard best practices for neural search systems.

This skill provides robust, production-grade patterns for building fault-tolerant search service integrations, specifically targeting Exa. It covers implementing query fallback chains, circuit breakers to prevent cascading failures, exponential backoff for retries, and graceful degradation levels. Use this when designing mission-critical search features that must remain available and functional even if underlying services fail, rate limit, or suffer performance degradation.

A comprehensive guide to implementing production-ready patterns for the Exa-js SDK. Covers critical architectural concerns such as client singletons, type-safe wrapper functions, advanced error handling, exponential backoff retry logic, and robust response validation using Zod. Use this when integrating Exa APIs into large-scale applications or establishing team coding standards for maximum stability and maintainability.