Working with Apache Camel means dealing with over 300 components, dozens of Enterprise Integration Patterns, multiple DSL formats, and a rich set of configuration options. Keeping all of that in your head while writing integration routes is not trivial, and AI coding assistants have become a natural companion for this kind of work. The problem is that general-purpose LLMs lack deep, structured knowledge of Camel’s catalog, its component options, its URI syntax rules, and its security best practices.

Continue reading ❯

CAMELAITOOLING

In 2025, Apache Camel’s adoption continued to surge, as shown by monthly download statistics from Maven Central. The graph below tracks downloads over the last 12 months (December 2024 to December 2025). It starts at approximately 29 million downloads in December 2024 and shows steady overall growth, with some fluctuations along the way. Monthly figures rose progressively, reaching peaks around 47 million in July and over 50 million in October, before settling at about 44.

Continue reading ❯

ROADMAP

In this blogpost, we’ll walk through a modern approach to developing Apache Camel applications. We’ll build a distributed log analyzer that automatically detects errors and uses an LLM for root cause analysis. Along the way, we’ll explore how tools like Camel JBang and Kaoto make development incredibly productive, and why Apache Camel’s YAML DSL is a perfect match for LLM-assisted development. Why Apache Camel? Before diving into the implementation, let’s address why Apache Camel is an excellent choice for this kind of system.

Continue reading ❯

CAMELAIKAFKAINFINISPANOPENTELEMETRY