Templates
Running Agents in production is hard, we need to:
- Serve them using an application like FastApi, Django or Streamlit.
- Manage their sessions, memory and knowlege in a database.
- Monitor, evaluate and improve their performance.
Phidata not only makes building Agents easy but also provides pre-built templates for agentic systems that you can deploy to AWS. Here’s how they work:
- Create your codebase using a template:
phi ws create
- Run your application locally:
phi ws up
- Run your application on AWS:
phi ws up prd:aws
We strongly believe that the data used by Agents should be stored securely inside your VPC.
We fully support BYOC (Bring Your Own Cloud) and encourage you to use your own AWS account.
Agent App
Let’s build an agent-app
which includes a Streamlit UI, FastApi server and Postgres database for memory and knowledge. Run it locally using docker or deploy to production on AWS.
Setup
Create a virtual environment
Install phidata
Install docker
Install docker desktop to run your app locally
Export your OpenAI key
You can get an API key from here.
Create your codebase
Create your codebase using the agent-app
template
This will create a folder agent-app
with the following structure:
Test your Agents using Streamlit
Streamlit allows us to build micro front-ends for testing our Agents. Start the app
using:
Press Enter to confirm and give a few minutes for the image to download (only the first time). Verify container status and view logs on the docker dashboard.
- Open localhost:8501 to view your AI Agent.
- The streamlit apps are defined in the
app
folder - The
Agents
are defined in theagents
folder.
Serve your Agents using FastApi
Streamlit is great for building micro front-ends but any production application will be built using a front-end framework like next.js backed by a RestApi built using FastApi.
Your Agent App comes ready-to-use with FastApi endpoints. Start the api
using:
- Open localhost:8000/docs to view the API Endpoints.
- Test the
/v1/playground/agent/run
endpoint with
Building your AI Product
The agent-app
comes with common endpoints that you can use to build your AI product. This API is developed in close collaboration with real AI Apps and are a great starting point.
The general workflow is:
- Your front-end/product will call the
/v1/playground/agent/run
to run Agents. - Using the
session_id
returned, your product can continue and serve chats to its users.
Delete local resources
Play around and stop the workspace using:
or stop individual Apps using:
Next
Congratulations on running an Agent App locally. Next Steps:
- Run your Agent App on AWS
- Read how to update workspace settings
- Read how to create a git repository for your workspace
- Read how to manage the development application
- Read how to format and validate your code
- Read how to add python libraries
- Chat with us on discord
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