May 2026 monthly summary for firecrawl/firecrawl: Delivered a Proxy Usage Tracking and Backfill feature to improve proxy billing accuracy and scraping correctness, and executed data integrity improvements for cached scrapes. The work provides clearer proxy usage telemetry, ensures historical data consistency, and strengthens billing reporting for scraped data.

2026-04 monthly summary for firecrawl/firecrawl: Focused on delivering a high-value SDK improvement that enhances reliability for client integrations and long-running operations. Implemented explicit Axios timeout handling in the JavaScript SDK to provide better control over request durations and improved error handling for long-running operations. The change was released alongside a version bump to reflect the new capability and ensure downstream compatibility. No major user-reported bugs fixed this month; ongoing stability work remains scoped to the SDK’s timeout and error semantics. Overall, this work reduces stalled requests, improves developer experience, and strengthens reliability for production deployments.

March 2026 monthly summary for Firecrawl projects focused on stability, reliability, and performance improvements across two repositories. Key architecture changes reduced maintenance burden while enhancing observability and resilience in crawling, job processing, and indexing. The month included significant features and bug fixes that delivered measurable business value: improved system stability under load, clearer error telemetry for faster remediation, and more flexible indexing in restricted environments.

February 2026: Delivered core improvements to A/B testing, scraping task tracking, concurrency reliability, and API stability across mendableai/firecrawl and firecrawl/firecrawl. The work enables faster experimentation, more reliable scraping workflows, and higher throughput with improved observability, driving stronger business outcomes and reduced operational risk.

In 2026-01, delivered two key features in mendableai/firecrawl focusing on performance and efficiency: Map Results Loading Optimization and Concurrency Queue Management Enhancement. Map Results Loading Optimization reduces unnecessary API calls by fetching additional pages only when the first page contains results, lowering latency and server load for empty-result scenarios. Concurrency Queue Management Enhancement introduces logic to promote jobs within concurrency limits, improving processing throughput. These changes were implemented via commits a422e1e14c56e4944c0b3d882a2b2ab6cd0e3685 and d86607c2752e2c6d35c38916f3d9d11d33b61aa1. Overall impact: better user experience when viewing map results, faster job processing, and reduced resource consumption, contributing to lower operational costs and higher reliability. Technologies/skills demonstrated: API-level feature development, concurrency control, performance optimization, and code hygiene through targeted commits and incremental improvements.