Implement processors in Mastra
Relying on “good prompts” alone to keep an agent safe and consistent doesn’t scale—real apps need a dependable way to shape what goes in and what comes out. In this workshop, you’ll learn how Mastra processors give you that control by running input processors before the LLM sees a message and output processors before a response reaches your users.
We’ll implement practical input processors to normalize and validate requests (think: cleaning up text, blocking unsafe content, trimming token-heavy context, or adjusting system messages). You’ll see where input processors sit in the pipeline, how execution order affects outcomes, and how to use per-step processing when you need to make decisions during the agentic loop (like switching models or changing tool availability mid-run).
Then we’ll build output processors that make responses safer and more reliable—filtering or transforming final messages, attaching helpful metadata, and handling streaming output as it arrives. We’ll also cover how output processors can abort or request retries to enforce quality and guardrails, so your app doesn’t ship broken or non-compliant responses.
You’ll leave with a clear mental model of when to use input vs. output processors, plus working patterns you can reuse to add consistency, safety, and cost control to any Mastra agent.
Hosted by
Alex Booker, Developer Experience at Mastra
Daniel Lew, Staff Software Engineer at Mastra
Recording and code examples will be available to everyone who registers.