context-chef-middleware

To prevent context overflow in the Vercel AI SDK, you can wrap your model with a middleware that summarizes older messages and truncates oversized tool outputs, keeping token usage within a configured budget automatically.

You can truncate oversized tool results in AI SDK agents by applying a middleware that preserves the head and tail portions of large tool outputs. This reduces context bloat and hallucination risks while optionally storing the full output for later retrieval.

The best way to compress chat history for Vercel AI SDK generateText calls is using a middleware that employs a cheaper model to summarize older messages. This happens automatically when the prompt exceeds your token budget, without changing your existing code structure.

Yes, you can use context compression middleware with existing Vercel AI SDK v6+ projects by wrapping your current language model once with a withContextChef function. You continue using your standard generateText and streamText calls without further code changes.

You can inject dynamic state into a compressed AI prompt by configuring optional hooks in your context management middleware. After automatic history compression, a transform hook can apply metadata or RAG additions to the final prompt context.

Your Vercel AI SDK prompt exceeds the token limit because large chat histories and oversized tool results accumulate in the context window. Applying a middleware to compress history and truncate tool outputs resolves this bloat while staying within your token budget.