context-engineering

You can manage persistent memory by implementing a file-system-like architecture to store and retrieve LLM context as governed, traceable artifacts across agent sessions. This ensures long-running AI agents consistently remember past interactions and learned facts.

Context engineering for LLMs is the structured management of context windows, history, memory, and scratchpad tiers. You need it when building agentic AI that requires governed, traceable context artifacts to maintain consistency and reliability across sessions.

Build a context pipeline by orchestrating Constructor, Updater, and Evaluator components to manage your LLM token budget. This framework intelligently processes and updates context, treating it as a governed file system abstraction.

Yes, you can implement a file-system-based context engineering architecture to govern LLM context artifacts. This approach allows you to structure memory systems and context repositories as persistent, traceable items within agentic AI projects.

The best way to store LLM context history is using a context repository that maintains structured memory tiers. By triggering file-system-based context engineering, you ensure context artifacts remain persistent, accessible, and governed across multiple agent sessions.

No external dependencies are required to set up a governed LLM context repository. You can initialize the context repository for your project by running the included setup script to start managing your agentic AI context pipelines.