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Quickstart: Durable Agent

This quickstart shows how to run a durable agent built using Dapr Agents and Catalyst Cloud. The agent acts as a travel assistant that can search for flights and hotels, maintain conversation memory, and persist execution state across restarts.

You will learn how to:

  • Provision a Catalyst project and related resources using the Diagrid CLI.
  • Run a durable AI agent with tool-calling capabilities using Dapr Agents with Catalyst Cloud
  • Monitor parallel tool execution in the Catalyst web console

1. Prerequisites​

Before you proceed, ensure you have the following prerequisites installed:

2. Log in to Catalyst​

Authenticate to Diagrid Catalyst using the following command:

diagrid login

This command opens a new browser window where you log into Catalyst. Once logged in, you'll be shown a confirmation code (matching the code in your terminal) that you need to confirm.

Confirm your user details are correct using the following command:

diagrid whoami

The expected output contains the name of the organization, your user name, and the Catalyst API endpoint.

3. Clone Quickstart Code​

Clone the quickstart code from GitHub:

git clone https://github.com/diagridio/catalyst-quickstarts.git

Navigate to the quickstart directory:

cd catalyst-quickstarts/durable-agent/python

4. Configure OpenAI API Key​

Add your OpenAI API key to resources/agent-llm-provider.yaml:

metadata:
  - name: key
    value: "YOUR_OPENAI_API_KEY"

Optionally, to use Anthropic instead of OpenAI, update resources/agent-llm-provider.yaml with:

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: anthropic
spec:
  type: conversation.anthropic
  metadata:
  - name: key
    value: "YOUR_ANTHROPIC_API_KEY"
  - name: model
    value: claude-3-5-sonnet-20240620

5. Install Dependencies​

Create a virtual environment:

python -m venv .venv

Activate the virtual environment:

source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

6. Run with Catalyst Cloud​

The diagrid dev run command creates your Catalyst Cloud project (if needed), provisions resources (App IDs, Components, managed state stores and pubsub), configures environment variables, and sets up the connection between your local environment and Catalyst Cloud.

diagrid dev run -f dev-python-durable-agent.yaml --project dev-python-durable-agent --approve
tip

Wait for the following log output to confirm the agent is running before proceeding:

Uvicorn running on http://0.0.0.0:8001 (Press CTRL+C to quit)
== APP - travel-assistant-agent == Travel Assistant Agent is running

7. Interact with the Agent​

Test the Durable Agent using the terminal or the REST Client extension with the test.rest file.

7.1 Example 1: Multi-city flights and hotels​

Open a new terminal and trigger the agent via REST API:

curl -i -X POST http://localhost:8001/agent/run \
  -H "Content-Type: application/json" \
  -d '{"task": "Find me flights and hotels to London and Amsterdam"}'

At a high-level, the agent will:

  1. Process your flight and hotels request
  2. Execute the search_flights tool in parallel for both cities
  3. Return flight options with pricing to the LLM and plan next steps
  4. Execute the search_hotels tool in parallel for both cities
  5. At every step, persist execution state and conversation history to Catalyst
  6. Return flight and hotel options

The response will include flight and hotel search results for both London and Amsterdam with pricing details.

7.2 Example 2: Single destination​

The agent adapts its workflow based on your input. You can search for flights only, hotels only, or both. For example, search for a flight and a hotel to a single destination:

curl -i -X POST http://localhost:8001/agent/run \
  -H "Content-Type: application/json" \
  -d '{"task": "Find me flights and hotels to London"}'

8. View in the Catalyst web console​

Open the Workflow viewer in the Catalyst Cloud web console and navigate to the Workflows section. Select the workflow instance that corresponds to your agent execution.

The Catalyst workflow visualizer shows each step the agent takes during execution. You can inspect every step with its inputs and outputs — including inputs to the LLM, generated responses, and tool call parameters — making it easy to understand and debug agent behavior.

workflow viewer

9. Clean Up​

Stop the running application by pressing Ctrl+C in the terminal where diagrid dev run is running.

Delete the Catalyst Cloud project to clean up all provisioned resources:

diagrid project delete dev-python-durable-agent

Summary​

In this quickstart, you:

  • Ran a durable AI agent based on Dapr Agents and connected to Catalyst Cloud for agent durability
  • Observed parallel tool execution and workflow visualization in the Catalyst web console

Next steps​