Context engineering relies on a combination of retrieval, storage, and orchestration tools rather than a single library. At the core are vector databases, which enable external memory and selective retrieval. Systems like Milvus and its managed version, Zilliz Cloud, are commonly used to store embeddings and retrieve the most relevant context efficiently.
Other supporting tools include embedding models, document chunkers, rerankers, and evaluation pipelines. Chunking tools help split large documents into retrievable units. Rerankers help prioritize retrieved results so only the most relevant ones enter the prompt. Evaluation tools help detect when longer context actually hurts performance, which is critical for tuning retrieval size and summaries.
Finally, orchestration logic—often written directly in application code—ties everything together. This logic decides when to retrieve, how much to retrieve, when to summarize, and when to reset context. Context engineering is less about adopting a single framework and more about assembling the right components into a disciplined workflow. With vector databases handling memory and retrieval, teams can focus on making context predictable, minimal, and effective.