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Quickstart: Multi-Agent Workflow

This quickstart showcases how to build a durable, multi-agent workflow using Dapr Agents and Workflows. The workflow acts as a customer support system that triages support tickets and provides troubleshooting resolutions through two cooperating AI agents. Start by developing locally with Dapr and the Diagrid Dashboard, then optionally onboard to Catalyst Online for managed workflow execution.

multi-agent workflow

Part 1: Develop Locally with Dapr​

Develop and test your multi-agent workflows locally using Dapr and the Diagrid Dashboard for workflow visualization and debugging.

1

Prerequisites

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

2

Install Diagrid CLI

Download and install the Diagrid CLI:

curl -o- https://downloads.diagrid.io/cli/install.sh | bash

Move the diagrid binary into your path:

sudo mv ./diagrid /usr/local/bin

Verify the installation:

diagrid version
3

Set Up Diagrid Dashboard

Run the dashboard:

docker run \
  -p 8080:8080 \
  ghcr.io/diagridio/diagrid-dashboard:0.0.1

Access the dashboard at http://localhost:8080.

4

Clone Quickstart Code

Clone the quickstart code from the Diagrid Labs GitHub repository:

diagrid dev quickstart --type multi-agent-workflow --language python

Navigate to the quickstart directory:

cd multi-agent-workflow-python
5

Configure OpenAI API Key

Add your OpenAI API key to resources/openai.yaml:

metadata:
  - name: key
    value: "YOUR_OPENAI_API_KEY"
6

Install Dependencies

Create and activate a Python virtual environment:

python -m venv venv
source venv/bin/activate  # MacOS/Linux
# Or: venv\Scripts\activate  # Windows
note

Windows users: Use venv\Scripts\Activate.ps1 (PowerShell) or venv\Scripts\activate.bat (Command Prompt). Use conda, pipenv, or your preferred tool if desired.

Install requirements:

pip install -r requirements.txt
7

Run Locally with Dapr

Run your application with Dapr locally. Make sure your state component is in the components directory.

dapr run --app-id customer-support-system --resources-path resources --app-port 5001 --dapr-http-port 3500 -- python main.py

Your application is now running locally with Dapr. You can view workflow executions in the Diagrid Dashboard.

8

Interact with Multi-Agent Workflow

With the quickstart running locally, test the Multi-Agent Workflow using curl or the REST Client extension with the test.rest file.

Upon successful execution, the customer-support-workflow logs will show both agents working in sequence: the Triage Agent validating entitlement and urgency, followed by the Expert Agent analyzing the environment and generating a resolution.

Start multi-agent workflow​

Open a new terminal and start a customer support workflow:

curl -i -X POST http://localhost:5001/workflow/start \
  -H "Content-Type: application/json" \
  -d '{"customer": "Alice", "issue": "My Dapr system fails to start in production."}'

This command will block until the agent completes the workflow that has been started. The workflow will:

  1. Run the Triage Agent to check customer entitlement using the check_entitlement tool and assess urgency based on the issue description
  2. If entitled, run the Expert Agent to retrieve environment details using the get_customer_environment tool and generate a detailed resolution
  3. Return a customer-ready response with the proposed fix and urgency level
  4. Persist all execution state and agent conversation memory in the local state stores

View in Dashboard​

Navigate to http://localhost:8080 to see your workflow executions in the Diagrid Dashboard. You can:

  • List all workflow executions for the customer-support-system app
  • Filter and page through executions
  • Drill into a single execution to see its status, metadata, and lifecycle

Test non-entitled user​

Try a query with a customer who does not have an entitlement to see how the workflow handles rejection by skipping the Expert Agent step:

curl -i -X POST http://localhost:5001/workflow/start \
  -H "Content-Type: application/json" \
  -d '{"customer": "Bob", "issue": "The Kubernetes Jobs controller keeps crashing on startup, in production!"}'

Observe in the Diagrid Dashboard how execution ends after the Triage Agent reports the missing entitlement.

Part 2: Onboard to Catalyst Online​

Once you've developed and tested your multi-agent workflows locally, onboard to Catalyst Online for managed workflow execution and additional features.

1

Log in to Catalyst Online

Authenticate to Diagrid Catalyst Online:

diagrid login

Confirm your organization and user details are correct:

diagrid whoami
2

Onboard to Catalyst Online

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

diagrid dev run -f dev-python-multi-agent-workflow.yaml --project dev-python-multi-agent-workflow
3

Interact with Multi-Agent Workflow (Catalyst Online)

Test the Multi-Agent Workflow API as before. Workflows are now executed by Catalyst Online:

curl -i -X POST http://localhost:5001/workflow/start \
  -H "Content-Type: application/json" \
  -d '{"customer": "Alice", "issue": "My Dapr system fails to start in production."}'

Monitor in Catalyst Dashboard​

Navigate to the Catalyst web console to monitor your workflow execution.

1. View Workflow Execution

Use the Workflows section to observe real-time progress. You can examine each activity with its input, output, and timing:

workflow viewer

2. Examine State Storage

Catalyst maintains state stores that persist workflow and agent data:

  • statestore - Workflow execution state and retry information
  • memory-state - Conversation history and context

Inspect key/value data in the Diagrid KV Store data explorer.

3. View Application Map

The Application Map visualizes how the customer support system interacts with Catalyst-managed components and dependencies.

View detailed workflow logs using the Diagrid CLI:

diagrid workflow get $WORKFLOW_ID --app-id customer-support-system

Clean Up​

1

Clean Up Local ResourcesOptional

Stop the Diagrid Dashboard:

docker stop $(docker ps -q --filter ancestor=diagrid-dashboard:latest)

Remove the quickstart directory if desired:

cd ..
rm -rf multi-agent-workflow-quickstart-python
2

Offload from Catalyst OnlineOptional

To disconnect from Catalyst Online and remove the provisioned resources:

diagrid dev stop --project dev-python-multi-agent-workflow

This command will remove the connection between your local environment and Catalyst Online.

note

The diagrid dev stop command does not delete the Catalyst Online project and contained resources. To delete the entire project, use diagrid project delete <project-name>.