hai-ai

You can manage memory and compress context for long-running LLM conversations by using memory extraction, object isolation, and session persistence, integrating with vector stores to maintain scalable and context-aware AI workflows.

To set up an MCP server for streaming HTTP, SSE, or Stdio responses, use the unified API surface that manages resource registration and prompt management while applying the init, use, and close lifecycle.

Yes, you can define and register custom tools for LLM invocation across Node.js and browser environments using a registry-driven approach that standardizes tool calls and ensures safer error handling via HaiResult.

The best way to orchestrate end-to-end AI workflows with unified LLM calling is to use a cohesive API that combines chat and chatStream interactions with optional tool integration, memory management, and context compression.

Yes, configuration validation is handled through Zod schemas to ensure standardized setup, while the lifecycle of init, use, and close manages safe error handling and consistent API interactions across sessions.

Using a unified API for AI assistants consolidates LLM calling, tool registration, and memory management into one cohesive surface, eliminating fragmented libraries and ensuring consistent context handling and safer error handling.