10 AI frameworks for context engineering
This chapter covers
- How AI frameworks and platforms support the construction of context-aware applications.
- A taxonomy for organizing the framework landscape: AI application frameworks, agent orchestration frameworks, and AI application platforms.
- The main design trade-offs involved in selecting frameworks for context engineering.
- Representative features of widely used frameworks and platforms from a context engineering perspective.
The previous chapters examined the main building blocks of context engineering, including instructions, external knowledge, tools, memory, prompts, context management, orchestration, evaluation, observability, governance, and operations. In practice, developers rarely implement all these capabilities from scratch. They usually rely on frameworks and platforms that provide reusable abstractions for building applications with LLMs and AI agents.
This chapter organizes that ecosystem into three groups: AI application frameworks, agent orchestration frameworks, and AI application platforms. The chapter does not attempt to document every feature, extension, or configuration option available in each framework. Instead, it focuses on the most relevant and illustrative capabilities from a context engineering perspective to help readers understand the framework landscape and select technologies that meet their architectural and operational requirements.