AIAgent

id: simple_summarizer_agent
namespace: company.ai

inputs:
  - id: summary_length
    displayName: Summary Length
    type: SELECT
    defaults: medium
    values:
      - short
      - medium
      - long

  - id: language
    displayName: Language ISO code
    type: SELECT
    defaults: en
    values:
      - en
      - fr
      - de
      - es
      - it
      - ru
      - ja

  - id: text
    type: STRING
    displayName: Text to summarize
    defaults: |
      Kestra is an open-source orchestration platform that:
      - Allows you to define workflows declaratively in YAML
      - Allows non-developers to automate tasks with a no-code interface
      - Keeps everything versioned and governed, so it stays secure and auditable
      - Extends easily for custom use cases through plugins and custom scripts.

      Kestra follows a "start simple and grow as needed" philosophy. You can schedule a basic workflow in a few minutes, then later add Python scripts, Docker containers, or complicated branching logic if the situation calls for it.

tasks:
  - id: multilingual_agent
    type: io.kestra.plugin.ai.agent.AIAgent
    systemMessage: |
      You are a precise technical assistant.
      Produce a {{ inputs.summary_length }} summary in {{ inputs.language }}.
      Keep it factual, remove fluff, and avoid marketing language.
      If the input is empty or non-text, return a one-sentence explanation.
      Output format:
      - 1-2 sentences for 'short'
      - 2-5 sentences for 'medium'
      - Up to 5 paragraphs for 'long'
    prompt: |
      Summarize the following content: {{ inputs.text }}

  - id: english_brevity
    type: io.kestra.plugin.ai.agent.AIAgent
    prompt: Generate exactly 1 sentence English summary of "{{ outputs.multilingual_agent.textOutput }}"

pluginDefaults:
  - type: io.kestra.plugin.ai.agent.AIAgent
    values:
      provider:
        type: io.kestra.plugin.ai.provider.GoogleGemini
        modelName: gemini-2.5-flash
        apiKey: "{{ kv('GEMINI_API_KEY') }}"

id: agent_with_docker_mcp_server_tool
namespace: company.ai

inputs:
  - id: prompt
    type: STRING
    defaults: What is the current UTC time?

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    prompt: "{{ inputs.prompt }}"
    provider:
      type: io.kestra.plugin.ai.provider.OpenAI
      apiKey: "{{ kv('OPENAI_API_KEY') }}"
      modelName: gpt-5-nano
    tools:
      - type: io.kestra.plugin.ai.tool.DockerMcpClient
        image: mcp/time

id: agent_with_memory
namespace: company.ai

tasks:
  - id: first_agent
    type: io.kestra.plugin.ai.agent.AIAgent
    prompt: Hi, my name is John and I live in New York!

  - id: second_agent
    type: io.kestra.plugin.ai.agent.AIAgent
    prompt: What's my name and where do I live?

pluginDefaults:
  - type: io.kestra.plugin.ai.agent.AIAgent
    values:
      provider:
        type: io.kestra.plugin.ai.provider.OpenAI
        apiKey: "{{ kv('OPENAI_API_KEY') }}"
        modelName: gpt-5-mini
      memory:
        type: io.kestra.plugin.ai.memory.KestraKVStore
        memoryId: JOHN
        ttl: PT1M
        messages: 5

id: agent_with_content_retriever
namespace: company.ai

inputs:
  - id: prompt
    type: STRING
    defaults: What is the latest Kestra release and what new features does it include? Name at least 3 new features added exactly in this release.

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    prompt: "{{ inputs.prompt }}"
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.5-flash
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
    contentRetrievers:
      - type: io.kestra.plugin.ai.retriever.TavilyWebSearch
        apiKey: "{{ kv('TAVILY_API_KEY') }}"

id: agent_with_structured_output
namespace: company.ai

inputs:
  - id: customer_ticket
    type: STRING
    defaults: >-
      I can't log into my account. It says my password is wrong, and the reset link never arrives.

tasks:
  - id: support_agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.MistralAI
      apiKey: "{{ kv('MISTRAL_API_KEY') }}"
      modelName: open-mistral-7b

    systemMessage: |
      You are a classifier that returns ONLY valid JSON matching the schema.
      Do not add explanations or extra keys.

    configuration:
      responseFormat:
        type: JSON
        jsonSchema:
          type: object
          required: ["category", "priority"]
          properties:
            category:
              type: string
              enum: ["ACCOUNT", "BILLING", "TECHNICAL", "GENERAL"]
            priority:
              type: string
              enum: ["LOW", "MEDIUM", "HIGH"]

    prompt: |
      Classify the following customer message:
        {{ inputs.customer_ticket }}

id: market_research_agent
namespace: company.ai

inputs:
  - id: prompt
    type: STRING
    defaults: |
      Research the latest trends in workflow and data orchestration.
      Use web search to gather current, reliable information from multiple sources.
      Then create a well-structured Markdown report that includes an introduction,
      key trends with short explanations, and a conclusion.
      Save the final report as `report.md` in the `/tmp` directory.

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash
    prompt: "{{ inputs.prompt }}"
    systemMessage: |
      You are a research assistant that must always follow this process:
      1. Use the TavilyWebSearch content retriever to gather the most relevant and up-to-date information for the user prompt. Do not invent information.
      2. Summarize and structure the findings clearly in Markdown format. Use headings, bullet points, and links when appropriate.
      3. Save the final Markdown report as `report.md` in the `/tmp` directory by using the provided filesystem tool.

      Important rules:
      - Never output raw text in your response. The final result must always be written to `report.md`.
      - If no useful results are retrieved, write a short note in `report.md` explaining that no information was found.
      - Do not attempt to bypass or ignore the retriever or the filesystem tool.

    contentRetrievers:
      - type: io.kestra.plugin.ai.retriever.TavilyWebSearch
        apiKey: "{{ kv('TAVILY_API_KEY') }}"
        maxResults: 10

    tools:
      - type: io.kestra.plugin.ai.tool.DockerMcpClient
        image: mcp/filesystem
        command: ["/tmp"]
        binds: ["{{workingDir}}:/tmp"] # mount host_path:container_path to access the generated report
    outputFiles:
      - report.md

id: agent_with_code_execution_stats
namespace: company.ai

inputs:
  - id: series
    type: STRING
    defaults: |
      12, 15, 15, 18, 21, 99, 102, 102, 104

tasks:
  - id: stats_agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

    systemMessage: |
      You are a data analyst.
      Always use the CodeExecution tool for computations.
      Then summarize clearly in English.

    prompt: |
      Here is a numeric series: {{ inputs.series }}
      1) Compute mean, median, min, max, and standard deviation.
      2) Detect outliers using a z-score greater than 2.
      3) Explain the distribution in 5-8 lines.

    tools:
      - type: io.kestra.plugin.ai.tool.CodeExecution
        apiKey: "{{ kv('RAPID_API_KEY') }}"

id: agent_with_google_custom_search_release_notes
namespace: company.ai

inputs:
  - id: prompt
    type: STRING
    defaults: |
      Find the most recent Kestra release and summarize:
      - release date
      - 5 major new features
      - 3 important bug fixes
      Answer in English.

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

    systemMessage: |
      You are a release-notes assistant.
      If you need up-to-date information, call GoogleCustomWebSearch.
      Summarize sources and avoid hallucinations.

    prompt: "{{ inputs.prompt }}"

    tools:
      - type: io.kestra.plugin.ai.tool.GoogleCustomWebSearch
        apiKey: "{{ kv('GOOGLE_SEARCH_API_KEY') }}"
        csi: "{{ kv('GOOGLE_SEARCH_CSI') }}"

id: incident_triage_orchestrator
namespace: company.ai

inputs:
  - id: incident
    type: STRING
    defaults: |
      The "billing-prod" SaaS data has been stale for 2 hours.
      We suspect an API extraction failure from an external provider.

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.OpenAI
      apiKey: "{{ kv('OPENAI_API_KEY') }}"
      modelName: gpt-5-mini

    systemMessage: |
      You are an incident triage agent.
      Decide which flow to run to mitigate the issue.
      Use the kestra_flow tool to trigger it with relevant inputs.

    prompt: |
      Incident:
      {{ inputs.incident }}

      You can run the following flows in the "prod.ops" namespace:
      - restart-billing-extract (inputs: service, reason)
      - run-billing-backfill (inputs: service, sinceHours)
      - notify-oncall (inputs: team, severity, message)

      Pick the best flow and execute it using the tool.

    tools:
      - type: io.kestra.plugin.ai.tool.KestraFlow

id: multi_flow_planner_agent
namespace: company.ai

inputs:
  - id: objective
    type: SELECT
    defaults: ingestion
    values:
      - ingestion
      - cleanup
      - alerting

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

    prompt: |
      User objective: {{ inputs.objective }}
      Execute the most appropriate flow for this objective.

    tools:
      - type: io.kestra.plugin.ai.tool.KestraFlow
        namespace: prod.data
        flowId: ingest-daily-snapshots
        description: Daily ingestion of snapshots

      - type: io.kestra.plugin.ai.tool.KestraFlow
        namespace: prod.data
        flowId: purge-stale-partitions
        description: Cleanup of obsolete partitions

      - type: io.kestra.plugin.ai.tool.KestraFlow
        namespace: prod.ops
        flowId: send-severity-alert
        description: Send an on-call alert

id: agent_using_kestra_task_self_healing
namespace: company.ai

inputs:
  - id: error_message
    type: STRING
    defaults: "Disk usage >= 95% on node worker-3"

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

    systemMessage: |
      You are a self-healing automation agent.
      When remediation is needed, call the KestraTask tool.

    prompt: |
      Detected issue: {{ inputs.error_message }}
      1) Propose a safe remediation action.
      2) Execute the corresponding task using the tool.

    tools:
      - type: io.kestra.plugin.ai.tool.KestraTask
        tasks:
          - id: cleanup
            type: io.kestra.plugin.scripts.shell.Commands
            commands:
              - "..."   # Placeholder: the agent will decide real commands.
            timeout: PT10M

id: agent_with_sse_mcp_places
namespace: company.ai

inputs:
  - id: city
    type: STRING
    defaults: Lyon, France
  - id: cuisine
    type: STRING
    defaults: "bistronomic"

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

    systemMessage: |
      You are a local guide.
      Use the MCP places tool to search restaurants.
      Return a short ranked list with brief reasons.

    prompt: |
      Find 3 {{ inputs.cuisine }} restaurants in {{ inputs.city }}.
      Criteria: rating > 4.5, quiet atmosphere, mid-range budget.
      Provide name, address, and two short reasons for each.

    tools:
      - type: io.kestra.plugin.ai.tool.SseMcpClient
        sseUrl: https://mcp.apify.com/?actors=compass/crawler-google-places
        timeout: PT3M
        headers:
          Authorization: Bearer {{ kv('APIFY_API_TOKEN') }}

id: agent_research_and_validate_forecast
namespace: company.ai

inputs:
  - id: topic
    type: STRING
    defaults: "workflow and data orchestration market"
  - id: year
    type: INT
    defaults: 2028

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ kv('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

    systemMessage: |
      You are a market research analyst.
      1) Use TavilyWebSearch to gather current market size and CAGR.
      2) Use CodeExecution to project the market size to the target year.
      3) Summarize in English with sources.

    prompt: |
      Topic: {{ inputs.topic }}
      1) Find credible sources for the current market size and CAGR.
      2) Project the market size for {{ inputs.year }} using the CAGR.
      3) Write a compact report (2 paragraphs) plus a list of sources.

    tools:
      - type: io.kestra.plugin.ai.tool.TavilyWebSearch
        apiKey: "{{ kv('TAVILY_API_KEY') }}"
      - type: io.kestra.plugin.ai.tool.CodeExecution
        apiKey: "{{ kv('RAPID_API_KEY') }}"