Quickstart

In about fifteen minutes you will build a small invoice checker:

an agent that reads an invoice and returns its fields as JSON,
a function that checks the line items add up to the total,
a workflow that runs the two in order,

and run it from the Wavefront Console and with curl. You need access to an app in the Wavefront Console, and an API key for one of the supported model providers.

1. Add a model

Open your app and go to LLM Repository → Add LLM.

Field	Example
Display Name	`Default chat model`
Inference Engine Type	Your provider, for example OpenAI GPT or Azure OpenAI
Model Type	LLM
LLM Model Name	The model or deployment name at your provider
API Key	Your provider key
Base URL, API Version	Only for providers that need them (Azure OpenAI, Ollama, vLLM)

Click Add Model, then copy the new entry’s id from the list. Agents refer to the model by this id, so the key never appears in your YAML.

2. Create the agent

Go to Agents → Create Agent. Enter an Agent ID (invoice-reader), pick a Namespace, and paste this YAML, replacing <your-model-id>:

invoice-reader.yaml

metadata:
  name: invoice-reader
  version: 1.0.0
  description: Reads an invoice and returns its key fields

agent:
  name: invoice-reader
  role: Invoice reader
  job: |
    You read invoices and return their key fields exactly as printed.
    Use null for a field that is not on the invoice. Amounts are numbers
    without currency symbols or thousands separators.
  model:
    provider: rootflo          # use a model from the LLM Repository…
    model_id: <your-model-id>  # …by its id
  settings:
    temperature: 0
  parser:                      # the shape of the answer
    name: invoice-fields
    fields:
      - name: vendor
        type: str
        description: Name of the company that issued the invoice
      - name: invoice_number
        type: str
        description: The invoice number
      - name: invoice_date
        type: str
        description: Invoice date as YYYY-MM-DD
      - name: total_amount
        type: float
        description: The invoice total
      - name: line_items
        type: array
        description: Each line of the invoice
        items:
          name: line_item
          type: object
          description: One line
          fields:
            - name: description
              type: str
              description: What was charged
            - name: amount
              type: float
              description: Amount for this line

The parser makes the agent return JSON with exactly these fields. Field descriptions matter: the model reads them.

Try it

Open the agent. The console shows its name in title case (invoice-reader becomes Invoice Reader). In the Ask anything box, paste an invoice and send it:

ACME Office Supplies Ltd. Invoice INV-2041, dated 3 March 2026.
1) Printer paper, 10 reams: 2,400.00  2) Toner cartridge: 3,150.00
3) Delivery: 250.00. Total due: 5,800.00

The reply is structured:

{
  "vendor": "ACME Office Supplies Ltd.",
  "invoice_number": "INV-2041",
  "invoice_date": "2026-03-03",
  "total_amount": 5800,
  "line_items": [
    { "description": "Printer paper, 10 reams", "amount": 2400 },
    { "description": "Toner cartridge", "amount": 3150 },
    { "description": "Delivery", "amount": 250 }
  ]
}

You can also attach a photo or a PDF of an invoice instead of pasting text: use + → Select Images or Select Documents.

Testing the invoice-reader agent in its chat panel

3. Create the function

The agent reads the invoice; a function does the arithmetic. Arithmetic, comparisons and decisions belong in functions: they are exact, testable and cost nothing to run. Go to Functions → Create Function. Name it check-invoice-total, paste the YAML below into the editor, and click Create Processor:

check-invoice-total.yaml

type: javascript
description: >-
  Checks that an invoice's line items add up to its total.
function:
  code: |
    export default function (input) {
      const items = Array.isArray(input.line_items) ? input.line_items : [];
      const sum = items.reduce((s, item) => s + Number(item.amount || 0), 0);
      const total = Number(input.total_amount || 0);
      const difference = Math.round((total - sum) * 100) / 100;
      return {
        invoice_number: input.invoice_number || null,
        vendor: input.vendor || null,
        total_amount: total,
        line_items_sum: Math.round(sum * 100) / 100,
        difference,
        status: Math.abs(difference) < 0.01 ? 'TOTAL_MATCHES' : 'TOTAL_MISMATCH',
      };
    }
input_schema:
  required:
    - total_amount
  properties:
    total_amount:
      type: number
      description: Invoice total, from the invoice reader
    line_items:
      type: array
      description: Line items, from the invoice reader

Test it from the function page with a sample input, for example the JSON reply from step 2. Copy the function’s id.

4. Create the workflow

Go to Workflows → Create Workflow. Enter a Workflow ID (invoice-check), pick the same namespace, and paste this YAML, replacing <your-model-id> and <your-function-id>:

invoice-check.yaml

metadata:
  name: invoice-check
  version: 1.0.0
  description: Reads an invoice and checks that its line items add up to the total

arium:
  agents:
    - name: invoice_reader
      role: Invoice reader
      job: |
        You read invoices and return their key fields exactly as printed.
        Use null for a field that is not on the invoice. Amounts are numbers
        without currency symbols or thousands separators.
      model:
        provider: rootflo
        model_id: <your-model-id>
      settings:
        temperature: 0
      parser:
        name: invoice-fields
        fields:
          - name: vendor
            type: str
            description: Name of the company that issued the invoice
          - name: invoice_number
            type: str
            description: The invoice number
          - name: invoice_date
            type: str
            description: Invoice date as YYYY-MM-DD
          - name: total_amount
            type: float
            description: The invoice total
          - name: line_items
            type: array
            description: Each line of the invoice
            items:
              name: line_item
              type: object
              description: One line
              fields:
                - name: description
                  type: str
                  description: What was charged
                - name: amount
                  type: float
                  description: Amount for this line

  function_nodes:
    - name: check_total
      function_name: trigger_message_processor   # runs one of your functions
      description: Checks the line items against the total
      input_filter: [invoice_reader]             # receives the agent's answer
      prefilled_params:
        message_processor_id: <your-function-id>

  workflow:
    start: invoice_reader
    edges:
      - from: invoice_reader
        to: [check_total]
    end: [check_total]

How to read it:

agents and function_nodes declare the steps; workflow wires them together.
input_filter: [invoice_reader] hands the agent’s JSON to the function. The function sees its fields directly: input.total_amount, input.line_items.
The workflow’s output is the output of its last node, check_total.

Run it in the console

Open the workflow. Its YAML is on the left; on the right is the same chat panel as for agents. Paste the invoice text from step 2 into Ask anything and send. (This workflow needs no variables; when one does, add them with + → Add variables.) The result:

{
  "invoice_number": "INV-2041",
  "vendor": "ACME Office Supplies Ltd.",
  "total_amount": 5800,
  "line_items_sum": 5800,
  "difference": 0,
  "status": "TOTAL_MATCHES"
}

Change a line amount in the input and run again to see TOTAL_MISMATCH.

Running the invoice-check workflow in the console

5. Run it from the API

Your application calls the app directly, with an app user (not your console account). Sign in to get a token:

APP=https://your-app.example.com

TOKEN=$(curl -s -X POST "$APP/floware/v1/authenticate" \
  -H 'Content-Type: application/json' \
  -d '{"email": "builder@example.com", "password": "…"}' \
  | jq -r '.data.user.access_token')

Then run the workflow by its id (shown on the workflow page):

curl -s -X POST "$APP/floware/v2/workflows/<workflow-id>/inference" \
  -H "Authorization: Bearer $TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{
    "inputs": [{ "role": "user", "content": "ACME Office Supplies Ltd. Invoice INV-2041 … Total due: 5,800.00" }],
    "variables": {}
  }'

Response

{
  "meta": { "status": "success", "code": 1, "error": null },
  "data": {
    "message": "Workflow inference completed successfully",
    "data": {
      "result": "{\"difference\": 0, \"invoice_number\": \"INV-2041\", … \"status\": \"TOTAL_MATCHES\"}",
      "workflow_id": "invoice-check",
      "execution_time": 2.38,
      "variables": {}
    }
  }
}

A workflow’s result is a string containing the last node’s output; parse it as JSON. An agent run through POST /floware/v2/agents/<agent-id>/inference returns result as an object when the agent has a parser.

Every response uses the same envelope: meta.status is success or failure, and on failure meta.error says why.

Signing in again with the same app user ends that user’s other sessions. Give each script or service its own user.

What you built

You used the pattern most Wavefront features follow: an agent gathers the facts, a function makes the call, a workflow runs them in order.

Next steps

Workflows in depth

Routers, sub-workflows, iterators and versions.

Read and write your data

Load the record to check from a database and save the result.

Rules as configuration

Move the tolerance into a configuration admins can edit.

Run in the background

Queue long runs and poll for their result.

Getting started

Models and agents

Workflows

Functions

Data, files and configuration

Run and integrate

Guides

Patterns and troubleshooting

Operations

1. Add a model

2. Create the agent

Try it

3. Create the function

4. Create the workflow

Run it in the console

5. Run it from the API

What you built

Next steps

Workflows in depth

Read and write your data

Rules as configuration

Run in the background

​1. Add a model

​2. Create the agent

​Try it

​3. Create the function

​4. Create the workflow

​Run it in the console

​5. Run it from the API

​What you built

​Next steps

Workflows in depth

Read and write your data

Rules as configuration

Run in the background

1. Add a model

2. Create the agent

Try it

3. Create the function

4. Create the workflow

Run it in the console

5. Run it from the API

What you built

Next steps