mastra

y: {https://jobs.ashbyhq.com/mastra/531a3fe8-556f-4b83-bee7-2037...} We get back to everyone.

engineer who has built agents and who can explain technical decisions clearly to engineers who haven't seen your code. Remote. We have a lot of EU customers and response-time SLAs, so US Eastern/Central or EU hours work best. Process: a call with Shane (co-founder/CPO, leads customer engineering), a conversation with a customer engineer, a 2-3 hour take-home, then you present it to us and get feedback. Apply: {https://jobs.ashbyhq.com/Mastra/05172cb0-47ec-44f9-9fe8-07d4...} We get back to everyone.

outing: You can access 600+ models from 40+ providers by specifying a model string (e.g., `openai/gpt-5.2-codex`) with TS autocomplete and fallbacks. - Guardrails: Low-latency input and output processors for prompt injection detection, PII redaction, and content moderation. The tricky thing here was the low-latency part. - Scorers: An async eval primitive for grading agent outputs. Users were asking how they should do evals. We wanted to make it easy to attach to Mastra agents, runnable in Mastra studio, and save results in Mastra storage. - Plus a few other features like AI tracing (per-call costing for Langfuse, Braintrust, etc), memory processors, a `.network()` method that turns any agent into a routing agent, and server adapters to integrate Mastra within an existing Express/Hono server. (That last one took a bit of time, we went down the ESM/CJS bundling rabbithole, ran into lots of monorepo issues, and ultimately opted for a more explicit approach.) Anyway, we'd love for you to try Mastra out and let us know what you think. You can get started with `npm create mastra@latest`. We'll be around and happy to answer any questions!

initially skeptical, but I tried writing an outline and it was pretty natural. So I spent the next couple weekends trying to hammer our posts into book form, and fill in the gaps. Surprisingly it worked. The initial version was 92 pages, in 27 very short chapters. We were going to call it Principles of AI Engineering but a founder author friend convinced me to use the word Agents instead, so Principles of Building AI Agents it was. The first reaction we got was surprise. We handed it out at the same meetups we’d gone to earlier. We started seeing people post book reviews on social media. A couple founder friends told us the book changed their agent architecture or their interface design. An engineer turned videographer came to our office and shot a mini-documentary. People asked us a lot how we’d keep the book up to date. We didn’t really know. Then MCP and Studio Ghibli happened, and we knew we needed to update it. I sat down in May to write for another weekend. I ended up with another 50 pages. In addition to MCP and image gen, there was a lot of stuff to write about web browsing, workflow streaming, code gen, agentic RAG. We republished the book as the 2nd edition. The single thing that people comment on the most is a page where I give a partial excerpt from the leaked Bolt.new system prompt. It’s the first time many people have read a production-grade prompt. The book has become pretty popular recently. We've given out thousands of copies at AI meetups and conferences, and it's even gone viral on LinkedIn (of all places). In terms of actual mechanics, I wrote the book in Notion, then pasted it into a desktop app called Vellum, which spits out ePubs and PDFs. Amazon’s KDP lets you upload those and publish your book (they’ll print it for you). Having a nice CI/CD helped, as did having docs and blog posts as sort of intermediate artifacts in a content pipeline. It meant that when I was writing I could pull from a warm cache. The process felt O(n) rather than O(n log n). Enjoy reading, and please let me know what you think! (You probably just want to download the book and read it in your favorite reader, rather than use Github's PDF reader) I’ll be starting on a third edition soon, and I want to know what topics feel like they’re missing, if anything feels outdated, and so on.

ges/mcp-d...)

point we just looked at each other and were like, why aren't we trying to make this part easier, and decided to work on Mastra. Demo video: https://www.youtube.com/watch?v=8o_Ejbcw5s8 One thing we heard from folks is that seeing input/output of every step, of every run of every workflow, is very useful. So we took XState and built a workflow graph primitive on top with OTel tracing. We wrote the APIs to make control flow explicit: `.step()` for branching, `.then()` for chaining, and `.after()` for merging. We also added .`.suspend()/.resume()` for human-in-the-loop. We abstracted the main RAG verbs like `.chunk()`, `embed()`, `.upsert(),’ `.query()`, and `rerank()` across document types and vector DBs. We shipped an eval runner with evals like completeness and relevance, plus the ability to write your own. Then we read the MemGPT paper and implemented agent memory on top of AI SDK with a `lastMessages` key, `topK` retrieval, and a `messageRange` for surrounding context (think `grep -C`). But we still weren’t sure whether our agents were behaving as expected, so we built a local dev playground that lets you curl agents/workflows, chat with agents, view evals and traces across runs, and iterate on prompts with an assistant. The playground uses a local storage layer powered by libsql (thanks Turso team!) and runs on localhost with `npm run dev` (no Docker). Mastra agents originally ran inside a Next.js app. But we noticed that AI teams’ development was increasingly decoupled from the rest of their organization, so we built Mastra so that you can also run it as a standalone endpoint or service. Some things people have been building so far: one user automates support for an iOS app he owns with tens of thousands of paying users. Another bundled Mastra inside an Electron app that ingests aerospace PDFs and outputs CAD diagrams. Another is building WhatsApp bots that let you chat with objects like your house. We did (for now) adopt an Elastic v2 license. The agent space is pretty new, and we wanted to let users do whatever they want with Mastra but prevent, eg, AWS from grabbing it. If you want to get started: - On npm: npm create mastra@latest - Github repo: https://github.com/mastra-ai/mastra - Demo video: https://www.youtube.com/watch?v=8o_Ejbcw5s8 - Our website homepage: https://mastra.ai (includes some nice diagrams and code samples on agents, RAG, and links to examples) - And our docs: https://mastra.ai/docs Excited to share Mastra with everyone here – let us know what you think!