Building an Agent API

The Agent Api let’s us serve agents using a FastApi server and store memory and knowledge in a Postgres database. Run it locally using docker or deploy to production on AWS.

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Setup

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Create your codebase

Create your codebase using the agent-api template

This will create a folder agent-api with the following structure:

agent-api                     # root directory
├── agents                  # add your Agents here
├── api                     # add fastApi routes here
├── db                      # add database tables here
├── Dockerfile              # Dockerfile for the application
├── pyproject.toml          # python project definition
├── requirements.txt        # python dependencies generated by pyproject.toml
├── scripts                 # helper scripts
├── utils                   # shared utilities
└── workspace               # phidata workspace directory
    ├── dev_resources.py    # dev resources running locally
    ├── prd_resources.py    # production resources running on AWS
    ├── secrets             # secrets
    └── settings.py         # phidata workspace settings

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Serve your Agents using FastApi

FastApi is an exceptional framework for building RestApis. Its fast, well-designed and loved by everyone using it. Most production applications are built using a front-end framework like next.js backed by a RestAPI, where FastApi shines.

Your codebase comes pre-configured with FastApi and PostgreSQL, along with some sample routes. Start your workspace 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:8000/docs to view the API Endpoints.
• Test the /v1/playground/agent/run endpoint with

{
  "message": "howdy",
  "agent_id": "example-agent",
  "stream": true
}

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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.

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Delete local resources

Play around and stop the workspace using:

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Next

Congratulations on running your AI API locally. Next Steps:

• Run your Agent API 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