Show HN: AgentRun: DSL to turn agents into workflows

mmiguelrios 1 day ago 4 commentsRead Article on github.com

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Hi HN,

I just open sourced the DSL that our harness in grep.ai uses to turn repeatable parts of agent work into workflows. You can combine tool calls, code, Jev-powered system one decisions for things like routing and screening evidence, and agents when a step needs more investigation.

Our harness uses the traces and retro notes agents leave behind when doing a job to figure out which parts can become a workflow. The idea is to make the work easier to understand and avoid paying for a full agent loop where one isn’t needed. For example, a research workflow can split a question into subquestions, send agents to research them in parallel, use Jev to screen the evidence, and have another agent write the report. You can inspect the steps, evaluate the evidence screening separately, or change one agent without rebuilding everything.

The DSL and examples are in our GitHub. There’s a scripted demo you can run without API keys: https://github.com/Parcha-ai/agentrun

You can also use it as a Pi extension to build, inspect, and run workflows: https://github.com/Parcha-ai/agentrun#use-it-in-pi

I would love to hear if this is useful to others.

More background on how AgentRun works in this video: https://www.youtube.com/watch?v=vOVhtGjtwpg. Or read about our use cases in this article: https://x.com/MiguelriosEN/status/2101029313906987422.

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Discussion (4 Comments)Read Original on HackerNews

JonathanAgosto•21 minutes ago

¡Wepa Miguel! ¡Felicidades y mucho éxito con el release!

Congratulations, and wishing you the best with this release!

JustFinishedBSG•about 3 hours ago

I'll study it as I am toying with "what should a workflow definition language look like".

My current vision, and prototype, is that it should be as close as possible to a "real" language as possible so that both the user and the agent know immediately how to use it and how it functions.

So for `pi` it means using typescript.

Then the UI is derived from the AST / code as much as possible and for things that aren't neatly possible like that I eventually add small semantic helpers that define the UI.

For example "plan -> execute" is:

      await flow.unroll(
      remaining.map(point => ({
          key: point.id,
          label: point.objective,
      })),
      async () => {
          for (const point of remaining) {
          await flow.item(point.id, async () =>
              await flow.agent(executePoint, {
              title: `Point ${point.id}`,
              prompt: point.objective,
              }));
          }
      },
      { title: `Plan r${planRevision}` },
      );

( simplified code ) in my implementation and `unroll` is only there to have a nice

      ● Plan r1 · 1/3 · active
       1. Inspect parser behavior
      ● 2. Add empty-input coverage
      ○ 3. Run focused checks

UI instead of a plain "Plan · 1/3" UI with no detail (which would happen if I just used a for loop, yes it works)

dummydummy1234•about 1 hour ago

How are you thinking about state management when you handle things? I have been playing around this and the state gets messy fast.

JustFinishedBSG•about 1 hour ago

Only state I keep is filesystem and last message (but even that is persisted in the filesystem). Each agent gets its own btrfs volume, when it’s done the “next” agent get the previous agent work mounted in its own filesystem ( and told about it ). Agent is also able to “promote” files if it wants and they are mounted in a more prominent place.

I don’t know yet if it’s a good solution. But only thinking in terms of files / filesystem sure make things easier.

Also makes branching “easier”: no handling of merging or conflicts, the receiving agent just gets N file systems and decides how to handle things.