Xpersona

Crawler Summary

Multi-Question RAG Workflow answer-first brief

This instructional workflow demonstrates how to build a multi‑step pipeline in Dify. It accepts a body of text, extracts all standalone questions using a parameter‑extraction node, loops through each question, retrieves relevant knowledge from a dataset, and uses an agent to draft an answer with citations. Finally, the answers are formatted into a structured reply. The template is intended for educational purposes to help new users understand loops, iterations, knowledge retrieval and agent nodes. Prepare a knowledge base/dataset in Dify and note its dataset ID; ensure retrieval is enabled. Configure the Parameter Extractor nodes with your preferred LLM provider (e.g., OpenAI) and the prompt to extract questions. In the retrieval nodes, set the dataset ID and choose the desired retrieval parameters (e.g., search type and top‑k). Provide an LLM provider for the agent that generates answers and final formatting. Capability contract not published. No trust telemetry is available yet. Last updated 4/16/2026.

Freshness

Last checked 4/16/2026

Best For

Multi-Question RAG Workflow is best for workflow, support, knowledge workflows where documented compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, Dify, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierDifySafety: 82/100

Multi-Question RAG Workflow

This instructional workflow demonstrates how to build a multi‑step pipeline in Dify. It accepts a body of text, extracts all standalone questions using a parameter‑extraction node, loops through each question, retrieves relevant knowledge from a dataset, and uses an agent to draft an answer with citations. Finally, the answers are formatted into a structured reply. The template is intended for educational purposes to help new users understand loops, iterations, knowledge retrieval and agent nodes. Prepare a knowledge base/dataset in Dify and note its dataset ID; ensure retrieval is enabled. Configure the Parameter Extractor nodes with your preferred LLM provider (e.g., OpenAI) and the prompt to extract questions. In the retrieval nodes, set the dataset ID and choose the desired retrieval parameters (e.g., search type and top‑k). Provide an LLM provider for the agent that generates answers and final formatting.

Public facts

2

Change events

0

Artifacts

0

Freshness

Apr 16, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 4/16/2026.

Trust evidence available

Trust score

Unknown

Compatibility

Profile only

Freshness

Apr 16, 2026

Vendor

Dify

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 4/16/2026.

View on Dify

Setup snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Dify

profilemedium
Observed Apr 16, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredDify

Extracted files

0

Examples

0

Snippets

0

Languages

en

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredDify

Docs source

Dify

Editorial quality

ready

This instructional workflow demonstrates how to build a multi‑step pipeline in Dify. It accepts a body of text, extracts all standalone questions using a parameter‑extraction node, loops through each question, retrieves relevant knowledge from a dataset, and uses an agent to draft an answer with citations. Finally, the answers are formatted into a structured reply. The template is intended for educational purposes to help new users understand loops, iterations, knowledge retrieval and agent nodes. Prepare a knowledge base/dataset in Dify and note its dataset ID; ensure retrieval is enabled. Configure the Parameter Extractor nodes with your preferred LLM provider (e.g., OpenAI) and the prompt to extract questions. In the retrieval nodes, set the dataset ID and choose the desired retrieval parameters (e.g., search type and top‑k). Provide an LLM provider for the agent that generates answers and final formatting.

Full README

Prepare a knowledge base/dataset in Dify and note its dataset ID; ensure retrieval is enabled.

Configure the Parameter Extractor nodes with your preferred LLM provider (e.g., OpenAI) and the prompt to extract questions.

In the retrieval nodes, set the dataset ID and choose the desired retrieval parameters (e.g., search type and top‑k).

Provide an LLM provider for the agent that generates answers and final formatting.

Deploy the workflow and input text containing multiple questions to see how the pipeline extracts, searches and answers.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingDify

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

No protocol metadata captured.

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/snapshot"
curl -s "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/contract"
curl -s "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Missingagent-directory
No close protocol neighbors were found.
Agent hubMore from this source
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": []
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "DIFY_MARKETPLACE",
      "generatedAt": "2026-04-17T04:15:53.039Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "workflow",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "support",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "knowledge",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "cohere",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "openai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "capability:workflow|supported|profile capability:support|supported|profile capability:knowledge|supported|profile capability:cohere|supported|profile capability:openai|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Dify",
    "href": "https://marketplace.dify.ai/api/v1/templates/d2d2ae92-d95b-4d55-b343-be411c3147d3",
    "sourceUrl": "https://marketplace.dify.ai/api/v1/templates/d2d2ae92-d95b-4d55-b343-be411c3147d3",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-16T06:45:12.142Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[]

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