If you've tried building AI agents for

production, you know the drill. It

starts with a simple hello world in

Python. But the moment you need to add

memory, tool calling, reliable

orchestration, or handle high

concurrency for an enterprise app, it

becomes a nightmare of spaghetti code

and timeouts. We need a way to

orchestrate complex agentic workflows

that are stable enough for enterprise

use but flexible enough for us

developers to hack on.

Today I'm showing you Astron agent. It's

an open-source enterprisegrade platform

that handles the heavy lifting of agent

orchestration. We're going to build a

multi-step market research agent from

scratch. Deploy it using Docker and I'll

share the code so you can spin it up

yourself.

First, let's get this running on your

local machine. Unlike cloud only tools,

you own this infrastructure. We are

following the Docker Compose guide from

the GitHub repo. Step one, clone the

repository.

Step two, navigate into the Docker

directory.

Step three, we need to set up our

environment variables.

Open the ENV file. This is where you

configure your database connections and

model API keys. For this demo, the

defaults work great, but make sure to

add your LLM provider key here. Now,

fire it up.

Once the containers are healthy, head to

localhost. Default login is usually

admin/123.

And just like that, we have a production

ready agent platform running locally.

I'm starting a new workflow. Our trigger

is a simple input variable company name.

Next, I'll drag in a two node. Astron

has a readytouse ecosystem that

integrates with standard tools. I'm

connecting the search tool to our input

variable. This will query the web for

the latest news on that company. Now we

pass that row data into an LLM node. I'm

selecting a generic efficient model.

Here in the prompt window, I'm using

Ginger two style templating to reference

the search results from the previous

step. My system prompt is simple.

Analyze the new snippets and extract key

market sentiment and risks. Here is

where Astron shines over simple chains.

I'm adding a logic node. If the

sentiment is negative, I want it to

trigger an alert branch. If positive, it

just proceeds to reporting. This visual

branching makes debugging logic errors

10 times easier than tracing async

Python calls. Let's hit debug. I'll type

in Tesla. You can see the workflow

lighting up in real time. The search

tool fires. Data flows to the LLM. And

boom, here's our structured output. A

workflow isn't useful if it stays in the

browser. Astron agent lets you publish

this workflow instantly as an API. Once

I hit publish, I get a production ready

API endpoint. I can now hook this

directly into my internal Slackbot, my

SAS back end, or even a front-end React

app. And because we deployed this via

Docker, you can scale the worker nodes

horizontally to handle thousands of

these requests concurrently. I've

exported the workflow JSON for this

market analyst agent and uploaded it to

GitHub. You can find the link in the

description.

I'm also contributing this to the

awesome Astron workflow collection. This

is a community repo where developers are

sharing their best agent architectures.

Here is my challenge to you. Clone the

Astron agent repo. Run the Docker

Compose command and build a workflow

that solves a problem you have. Then

submit a pull request to the awesome

list. If you are an enterprise developer

or just love open-source AI, this is a

tool you need to watch. Links are below.

Thanks for watching and happy building.