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Show HN: Mastra – Open-source JS agent framework, by the developers of Gatsby

(github.com)
433 points calcsam | 6 comments | 19 Feb 25 15:25 UTC | HN request time: 0.431s | source | bottom

Hi HN, we’re Sam, Shane, and Abhi, and we’re building Mastra (https://mastra.ai), an open-source JavaScript SDK for building agents on top of Vercel’s AI SDK.

You can start a Mastra project with `npm create mastra` and create workflow graphs that can suspend/resume, build a RAG pipeline and write evals, give agents memory, create multi-agent workflows, and view it all in a local playground.

Previously, we built Gatsby, the open-source React web framework. Later, we worked on an AI-powered CRM but it felt like we were having to roll all the AI bits (agentic workflows, evals, RAG) ourselves. We also noticed our friends building AI applications suffering from long iteration cycles: they were getting stuck debugging prompts, figuring out why their agents called (or didn’t call) tools, and writing lots of custom memory retrieval logic.

At some 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!

Show context
Palmik ◴[20 Feb 25 05:47 UTC] No.43111545[source]▶
The example from the landing page does not exactly spark joy:

    testWorkflow
     .step(llm)
       .then(decider)
       .then(agentOne)
       .then(workflow)
     .after(decider)
       .then(agentTwo)
       .then(workflow)
      .commit();
On a first glance, this looks like a very awkward way to represent the graph from the picture. And this is just a simple "workflow" (the structure of the graph does not depend on the results of the execution), not an agent.
replies(5): >>43111621 #>>43113904 #>>43113922 #>>43114354 #>>43116216 #
1. calcsam ◴[20 Feb 25 06:04 UTC] No.43111621[source]▶
Thanks! The conditional `when` clauses live on the steps, rather than being represented in the workflow, and in fact when we built this for an example, the last step being called depended on the results of the previous two steps.

How would you simplify this?

replies(2): >>43113743 #>>43113935 #
2. anentropic ◴[20 Feb 25 12:09 UTC] No.43113743[source]▶
I think the problem is that a 'fluent' chain of calls already expresses a sequence, so the way that 'after' resets the context to start a new branch feels very awkward ... like a GOTO or something

It's telling that the example relies on arbitrary indentation (which a linter will get rid of) to have some hope of comprehending it

Possibly this was all motivated by a desire to avoid nested structures above all?

But for a branching graph a nested structure is more natural. It'd also probably be nicer if the methods were on the task nodes instead of on the workflow, then you could avoid the 'step'/'then' distinction and have something like:

e.g.

    testWorkflow(
        llm
        .then(decider)
        .then(
            agentOne.then(workflow),
            agentTwo.then(workflow),
        )
    )
replies(1): >>43115706 #
3. jumski ◴[20 Feb 25 12:31 UTC] No.43113935[source]▶
I think it is just easier to comprehend if the edges/dependencies are explicit (as an array for example).
replies(1): >>43118303 #
4. calcsam ◴[20 Feb 25 15:17 UTC] No.43115706[source]▶
You’re right that the syntax was inspired by the desire to avoid nested structures. But the syntax here is interesting as well and fairly readable. Worth thinking about!
replies(1): >>43126122 #
5. calcsam ◴[20 Feb 25 18:31 UTC] No.43118303[source]▶
We have a ticket to allow this actually!
6. anentropic ◴[21 Feb 25 10:46 UTC] No.43126122{3}[source]▶
that example syntax is loosely based on CDK code for AWS Step Functions, since I had to write some recently

essentially you're building a DAG so it could be worth checking some other APIs which do a similar thing for inspiration

e.g. it looks like in Airflow you could write it as:

    chain(llm, decider, [agentOne, agentTwo], workflow)
https://airflow.apache.org/docs/apache-airflow/stable/core-c...