Search Resource

Note: This capability is now provided as an installable component. See the Components guide for how to install and use it.

Install: kdeps component install search

Usage: run: { component: { name: search, with: { query: "...", apiKey: "...", maxResults: 5 } } }

The Search component discovers content from the web via the Tavily AI-optimized search API, returning a list of results (title, URL, snippet) for downstream processing.

Component Inputs

Input	Type	Required	Default	Description
query	string	yes	—	Search query string
apiKey	string	yes	—	Tavily API key
maxResults	integer	no	5	Maximum number of results to return

Using the Search Component

run:
  component:
    name: search
    with:
      query: "kdeps AI agent framework"
      apiKey: "tvly-your-api-key"
      maxResults: 10

Access the result via output('<callerActionId>'). The result is a list of objects with title, url, and snippet fields.

---

Basic Usage

apiVersion: kdeps.io/v1
kind: Resource

metadata:
  actionId: findArticles
  name: Find Articles

run:
  component:
    name: search
    with:
      query: "{{ get('topic') }}"
      apiKey: "{{ env('TAVILY_API_KEY') }}"
      maxResults: 5

---

Result Map

{
  "query": "kdeps AI workflow",
  "results": [
    {
      "title": "Introduction to kdeps",
      "url": "https://kdeps.io/docs/intro",
      "snippet": "kdeps is an open-source AI workflow engine..."
    }
  ],
  "count": 1,
  "success": true
}

Field	Type	Description
query	string	The query string that was used.
results	[]object	Array of result items.
count	int	Number of results returned.
success	bool	true when the search completed without error.
error	string	Error message (only present when success is false).

Result item fields:

Field	Type	Description
title	string	Page title.
url	string	Page URL.
snippet	string	Excerpt or description.

---

Expression Support

All with: fields support KDeps expressions:

run:
  component:
    name: search
    with:
      query: "{{ get('user_query') }}"
      apiKey: "{{ env('TAVILY_API_KEY') }}"
      maxResults: "{{ get('result_count', 5) }}"

---

Full Example: Web Research Pipeline

This pipeline searches the web for a topic, scrapes the top result, and asks an LLM to summarize it.

# Step 1: Search for the topic
- apiVersion: kdeps.io/v1
  kind: Resource

  metadata:
    actionId: webSearch
    name: Web Search

  run:
    component:
      name: search
      with:
        query: "{{ get('research_topic') }}"
        apiKey: "{{ env('TAVILY_API_KEY') }}"
        maxResults: 3

# Step 2: Scrape the first result
- apiVersion: kdeps.io/v1
  kind: Resource

  metadata:
    actionId: scrapeTop
    name: Scrape Top Result
    requires:
      - webSearch

  run:
    component:
      name: scraper
      with:
        url: "{{ output('webSearch').results[0].url }}"
        timeout: 30

# Step 3: LLM summarizes the scraped content
- apiVersion: kdeps.io/v1
  kind: Resource

  metadata:
    actionId: summarise
    name: Summarise Research
    requires:
      - scrapeTop

  run:
    llm:
      prompt: |
        You are a research assistant. Summarise the following content about
        "{{ get('research_topic') }}":

        {{ output('scrapeTop').content }}

        Provide a concise 3-paragraph summary.

# Step 4: Return the summary
- apiVersion: kdeps.io/v1
  kind: Resource

  metadata:
    actionId: researchResponse
    name: Research Response
    requires:
      - summarise

  run:
    apiResponse:
      success: true
      response:
        summary: "{{ output('summarise') }}"
        source_url: "{{ output('webSearch').results[0].url }}"
        source_title: "{{ output('webSearch').results[0].title }}"