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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.
Tired of AI agents that only work in a demo? In this video, we’re setting up Astron Agent, an open-source, enterprise-grade platform for building and running complex AI workflows in production. We’ll walk through the full setup—from cloning the GitHub repo to launching a high-availability environment with Docker. 🚀 What You’ll Learn: -How to self-host Astron Agent using Docker. -Setting up the orchestration console and authentication. -Connecting agents to real tools and RPA. -Where to find pre-built workflow templates. 🔗 Links Mentioned: -Astron Agent GitHub: https://github.com/iflytek/astron-agent -Awesome Workflows: https://github.com/FenjuFu/Awesome-Astron-Workflow 🛠️ Deployment Commands Used: -git clone https://github.com/iflytek/astron-agent.git -cd docker/astronAgent -cp .env.example .env -docker compose -f docker-compose-with-auth.yaml up -d #AstronAgent #agent #workflow #ai #python