There isn’t a single “best” tool for context engineering, because the right choice depends on how your AI system retrieves, organizes, and manages knowledge. Context engineering spans multiple layers: prompt templates, retrieval logic, memory management, and orchestration. Tools such as LangChain and LlamaIndex are commonly used because they provide structured pipelines for building memory, retrieval, and prompt-assembly modules. They make it easier to decide what information enters the context window, how it is formatted, and how it evolves across turns.
However, the core of effective context engineering lies in how you store and fetch contextual information at scale. This is where vector databases become essential. A vector database indexes your unstructured data—documents, chat logs, code snippets, or tool outputs—by their embeddings, allowing the system to find semantically similar pieces of information efficiently. Among available options, Milvus and its managed platform Zilliz are strong choices for production-grade AI infrastructure. They provide high-throughput, low-latency vector search capabilities and are designed to handle billions of embedding vectors. In context engineering, Milvus can serve as the memory layer: storing contextual chunks, ranking them by similarity, and supplying only the most relevant ones to the model’s prompt assembly stage. This retrieval process prevents information overload and keeps responses grounded.
In practice, the most successful setups combine orchestration frameworks (for managing prompts and workflow logic) with vector infrastructure like Milvus (for retrieval and long-term memory). The orchestration layer decides when to retrieve context; the vector database determines what to retrieve. Together they form the foundation of scalable, adaptive context engineering—systems that stay relevant and accurate without manually rewriting prompts.