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Prerequisites

  • Python 3.12+
  • Docker (for PostgreSQL)

Create a project

1

Install the CLI

2

Initialize a new project

The CLI will prompt you for a location, template, and project name. Choose the simple-chatbot template to get started quickly.
3

Configure your environment

Open .env and add your OPENAI_API_KEY.
4

Install dependencies and start the server

The generated project uses uv for dependency management. The pyproject.toml created by aegra init defines your project’s dependencies, and uv handles the rest.
This starts a PostgreSQL container, applies database migrations, and launches the server with hot reload.
Your server is running at http://localhost:2026. Visit http://localhost:2026/docs to explore the API.
Always install aegra-cli directly — not the aegra meta-package. The aegra package on PyPI is a convenience wrapper that does not support version pinning.

Talk to your agent

Your LangGraph code works without changes:

Connect a frontend

Aegra implements the Agent Protocol, so it works with any compatible frontend:

Agent Chat UI

Open-source chat interface for agent interactions.

LangGraph Studio

Visual graph debugger and testing tool.

CopilotKit

In-app AI copilot framework via AG-UI protocol.

What’s next

Build your first agent

Hands-on tutorial: build, deploy, and interact with an agent step by step.

Streaming

Understand stream modes, SSE events, and reconnection.

Authentication

Add JWT, OAuth, or Firebase auth to your server.

Deployment

Deploy to Docker, PaaS, or Kubernetes.