The integration of Large Language Models (LLMs) into enterprise applications has become increasingly important. Whether you’re building intelligent document processing pipelines, automated customer support systems, or data privacy solutions, the ability to seamlessly connect your integration flows with AI capabilities is essential. Apache Camel 4.17 introduces the new camel-openai component, providing native integration with OpenAI and OpenAI-compatible APIs for chat completion. In this post, we’ll explore the component’s features and demonstrate a practical use case: Personal Identifiable Information (PII) redaction using structured output.

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AI

It’s the time of the year when we take a look back at 2025, and compile a brief summary (by numbers) of the Apache Camel project(s). You can find previous year 2024 numbers here. Camel 2025 in Numbers Number of Camel Core / Camel Spring Boot releases in 2025: 25 Number of Camel Quarkus releases in 2025: 18 Number of Camel Karavan releases in 2025: 3 Number of Camel K releases in 2025: 5

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ROADMAP

The Apache Camel website now generates markdown versions of all documentation pages following the llms.txt specification. This makes our documentation easily accessible to Large Language Models (LLMs) and AI coding assistants. What is llms.txt? The llms.txt specification is a standardized format that helps LLMs discover and consume website content efficiently. Similar to how robots.txt guides web crawlers and sitemap.xml helps search engines, llms.txt provides a structured entry point for AI systems to understand and access documentation.

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TOOLING