How should rag-with-crewai-flow be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

rag-with-crewai-flow answer-first brief

Rag implementation with crewai flow 🏗️ RAG Article App — Architecture & Workflow End-to-end architecture for an AI-powered article search and summarization app using CrewAI Flow, Firebase, BigQuery, and Vertex AI. --- 📐 High-Level Architecture --- 🔄 Detailed Flow — User Search --- 📰 User Browse Flow (No Search) [InBits] (https://www.inbits.co/) <br/> [GitHub Repo] (https://github.com/amitvermaknw/inbits) --- 🤖 CrewAI Agents Web Search Agent Summar Capability contract not published. No trust telemetry is available yet. Last updated 6/1/2026.

Freshness

Last checked 6/1/2026

Best For

rag-with-crewai-flow is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGitHubSafety: 66/100

rag-with-crewai-flow

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Jun 1, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 6/1/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Jun 1, 2026

Vendor

Amitvermaknw

Artifacts

Benchmarks

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Unpublished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 6/1/2026.

View Source

Setup snapshot

git clone https://github.com/amitvermaknw/rag-with-crewai-flow.git

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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

Amitvermaknw

profilemedium

Observed May 24, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed May 24, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

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-declaredGITHUB OPENCLEW

Extracted files

Examples

Snippets

Languages

python

Executable Examples

text

┌─────────────────────────────────────────────────────────────┐
│                        CLIENT (Mobile Web)                  │
└─────────────────────────┬───────────────────────────────────┘
                          │  POST /api/v1/search
                          ▼
┌─────────────────────────────────────────────────────────────┐
│              Cloud Run — FastAPI + CrewAI Flow              │
│                                                             │
│   ┌─────────────────────────────────────────────────────┐   │
│   │                  ArticleFlow                        │   │
│   │                                                     │   │
│   │  @start → check_bigquery_cache                      │   │
│   │      ↓                                              │   │
│   │  @router → route_after_cache                        │   │ 
│   │      ↙               ↘                              │   │
│   │  cache_hit         cache_miss                       │   │
│   │      ↓                 ↓                            │   │
│   │  return_result    fetch_from_web (Agent)            │   │
│   │                       ↓                             │   │
│   │                  summarize (Agent)                  │   │
│   │                       ↓                             │   │
│   │                  generate_embedding                 │   │
│   │                       ↓                             │   │
│   │                  save_to_firebase                   │   │
│   │                       ↓                             │   │
│   │                  save_to_bigquery                   │   │
│   │                       ↓                             │   │
│   │                  return_result                      │   │
│   └─────────────────────────────────────────────────────┘   │
└──────────┬──────────────────────┬───────────────────────────┘
           │                      │
           ▼                      ▼
┌─────────────────┐    ┌──────────────────────────────────

text

User types query in app
          │
          ▼
POST /api/v1/search {"query": "AI trends 2026"}
          │
          ▼
┌─────────────────────────────┐
│  ArticleFlow.kickoff_async  │
│  inputs: {query: "..."}     │
│  → maps to ArticleState     │
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  @start                     │
│  check_bigquery_cache()     │
│                             │
│  VECTOR_SEARCH() in BQ      │
│  with query embedding       │
└──────────┬──────────────────┘
           │
           ▼
┌─────────────────────────────┐
│  @router                    │
│  route_after_cache()        │
└────────┬──────────┬─────────┘
         │          │
    cache_hit    cache_miss
         │          │
         ▼          ▼
┌──────────┐  ┌─────────────────────────┐
│ return   │  │  @listen("cache_miss")  │
│ articles │  │  fetch_from_web()       │
│ from BQ  │  │                         │
└──────────┘  │  Web Search Agent       │
              │  → SerperDev Tool       │
              │  → Scrape Article       │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌─────────────────────────┐
              │  @listen(fetch_from_web)│
              │  summarize_article()    │
              │                         │
              │  Summary Agent          │
              │  → OpenAI LLM           │
              │  → Generate summary     │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌─────────────────────────┐
              │  @listen(summarize)     │
              │  generate_embedding()   │
              │                         │
              │  Vertex AI              │
              │  text-embedding-005     │
              │  → vector [0.1, 0.2...] │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌───────────────────────

text

User opens app
      │
      ▼
GET /api/v1/articles
      │
      ▼
Read directly from Firebase (Firestore)
      │
      ▼
Return [title, summary, thumbnail, url, published_at]
      │
      ▼
Display article feed in UI

text

Role    : Web Research Specialist
Tools   : SerperDevTool, ScrapeWebsiteTool
Goal    : Find and extract full article content from the web
Trigger : cache_miss in flow

text

Role    : Article Summarizer
LLM     : OpenAI
Goal    : Generate concise, readable article summary
Trigger : After web search agent completes

bash

# Build and deploy to Cloud Run
gcloud run deploy article-rag-app \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --set-env-vars GOOGLE_CLOUD_PROJECT=your-project-id

Docs & README

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

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Full README

🏗️ RAG Article App — Architecture & Workflow

End-to-end architecture for an AI-powered article search and summarization app using CrewAI Flow, Firebase, BigQuery, and Vertex AI.

📐 High-Level Architecture

┌─────────────────────────────────────────────────────────────┐
│                        CLIENT (Mobile Web)                  │
└─────────────────────────┬───────────────────────────────────┘
                          │  POST /api/v1/search
                          ▼
┌─────────────────────────────────────────────────────────────┐
│              Cloud Run — FastAPI + CrewAI Flow              │
│                                                             │
│   ┌─────────────────────────────────────────────────────┐   │
│   │                  ArticleFlow                        │   │
│   │                                                     │   │
│   │  @start → check_bigquery_cache                      │   │
│   │      ↓                                              │   │
│   │  @router → route_after_cache                        │   │ 
│   │      ↙               ↘                              │   │
│   │  cache_hit         cache_miss                       │   │
│   │      ↓                 ↓                            │   │
│   │  return_result    fetch_from_web (Agent)            │   │
│   │                       ↓                             │   │
│   │                  summarize (Agent)                  │   │
│   │                       ↓                             │   │
│   │                  generate_embedding                 │   │
│   │                       ↓                             │   │
│   │                  save_to_firebase                   │   │
│   │                       ↓                             │   │
│   │                  save_to_bigquery                   │   │
│   │                       ↓                             │   │
│   │                  return_result                      │   │
│   └─────────────────────────────────────────────────────┘   │
└──────────┬──────────────────────┬───────────────────────────┘
           │                      │
           ▼                      ▼
┌─────────────────┐    ┌─────────────────────────────────────┐
│    Firebase     │    │              BigQuery               │
│   (Firestore)   │    │                                     │
│                 │    │  articles table:                    │
│ - title         │    │  - title, summary, url              │
│ - summary       │    │  - full_content                     │
│ - url           │    │  - embedding (vector)               │
│ - thumbnail     │    │  - fetched_at                       │
│ - published_at  │    │                                     │
│                 │    │  VECTOR_SEARCH() for RAG            │
│ Used for:       │    │  Used for:                          │
│ Feed/Browse UI  │    │  Semantic Search + RAG              │
└─────────────────┘    └─────────────────────────────────────┘

🔄 Detailed Flow — User Search

User types query in app
          │
          ▼
POST /api/v1/search {"query": "AI trends 2026"}
          │
          ▼
┌─────────────────────────────┐
│  ArticleFlow.kickoff_async  │
│  inputs: {query: "..."}     │
│  → maps to ArticleState     │
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  @start                     │
│  check_bigquery_cache()     │
│                             │
│  VECTOR_SEARCH() in BQ      │
│  with query embedding       │
└──────────┬──────────────────┘
           │
           ▼
┌─────────────────────────────┐
│  @router                    │
│  route_after_cache()        │
└────────┬──────────┬─────────┘
         │          │
    cache_hit    cache_miss
         │          │
         ▼          ▼
┌──────────┐  ┌─────────────────────────┐
│ return   │  │  @listen("cache_miss")  │
│ articles │  │  fetch_from_web()       │
│ from BQ  │  │                         │
└──────────┘  │  Web Search Agent       │
              │  → SerperDev Tool       │
              │  → Scrape Article       │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌─────────────────────────┐
              │  @listen(fetch_from_web)│
              │  summarize_article()    │
              │                         │
              │  Summary Agent          │
              │  → OpenAI LLM           │
              │  → Generate summary     │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌─────────────────────────┐
              │  @listen(summarize)     │
              │  generate_embedding()   │
              │                         │
              │  Vertex AI              │
              │  text-embedding-005     │
              │  → vector [0.1, 0.2...] │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌─────────────────────────┐
              │  @listen(embedding)     │
              │  save_data()            │
              │                         │
              │  → Firebase (summary)   │
              │  → BigQuery (full+emb)  │
              └───────────┬─────────────┘
                          │
                          ▼
              ┌─────────────────────────┐
              │  @listen(save_data)     │
              │  return_result()        │
              │  → return to user       │
              └─────────────────────────┘

📰 User Browse Flow (No Search)

User opens app
      │
      ▼
GET /api/v1/articles
      │
      ▼
Read directly from Firebase (Firestore)
      │
      ▼
Return [title, summary, thumbnail, url, published_at]
      │
      ▼
Display article feed in UI

[InBits] (https://www.inbits.co/)
<br/> [GitHub Repo] (https://github.com/amitvermaknw/inbits)

🤖 CrewAI Agents

Web Search Agent

Role    : Web Research Specialist
Tools   : SerperDevTool, ScrapeWebsiteTool
Goal    : Find and extract full article content from the web
Trigger : cache_miss in flow

Summary Agent

Role    : Article Summarizer
LLM     : OpenAI
Goal    : Generate concise, readable article summary
Trigger : After web search agent completes

🗄️ Data Schema

Firebase (Firestore) — `articles` collection

| Field | Type | Purpose | |---|---|---| | id | string | URL hash | | title | string | Article title | | summary | string | AI generated summary | | url | string | Source URL | | thumbnail | string | Image URL | | published_at | timestamp | Publish date | | source | string | web_search / manual |

BigQuery — `articles` table

| Field | Type | Purpose | |---|---|---| | id | STRING | URL hash (matches Firebase) | | title | STRING | Article title | | summary | STRING | AI generated summary | | url | STRING | Source URL | | full_content | STRING | Complete article text | | embedding | ARRAY<FLOAT64> | Vector for semantic search | | fetched_at | TIMESTAMP | When fetched | | source | STRING | Origin of article |

💰 Cost Estimate (POC)

| Service | Usage | Cost | |---|---|---| | Cloud Run | Low traffic POC | ~$0 (free tier) | | Firebase Firestore | Lightweight docs | ~$0 (free tier) | | BigQuery Storage | 100MB | ~$0 (free tier 10GB) | | BigQuery Queries | < 1TB/month | ~$0 (free tier) | | Vertex AI Embeddings | Per 1K chars | ~$0.0001 | | Total POC | | ~$0–2/month |

⚠️ Vertex AI Vector Search NOT used — BigQuery VECTOR_SEARCH() used instead for POC cost savings. Migrate to Vertex AI Vector Search only when production low-latency (<50ms) is needed.

🚀 Deployment

# Build and deploy to Cloud Run
gcloud run deploy article-rag-app \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --set-env-vars GOOGLE_CLOUD_PROJECT=your-project-id

🔮 Future: Production Migration

POC (Now)                    Production (Later)
─────────────────────        ──────────────────────────
BigQuery VECTOR_SEARCH()  →  Vertex AI Vector Search
Single Cloud Run instance →  Cloud Run autoscaling
Manual embedding batch    →  Eventarc trigger on new doc

Contract & API

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

MissingGITHUB OPENCLEW

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/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.

Self-declaredprotocol-neighbors

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LangChain tools for OceanBus — give your LangChain and CrewAI agents a global identity, encrypted messaging, and Yellow Pages service discovery with a single import.

OPENCLAWoceanbuslangchainlangchain-tools

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://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-08T23:20:12.782Z"
    }
  },
  "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": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Amitvermaknw",
    "category": "vendor",
    "href": "https://github.com/amitvermaknw/rag-with-crewai-flow",
    "sourceUrl": "https://github.com/amitvermaknw/rag-with-crewai-flow",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-24T06:16:55.843Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-24T06:16:55.843Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-amitvermaknw-rag-with-crewai-flow/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[]

rag-with-crewai-flow answer-first brief

Executive Summary

Evidence Ledger

Release & Crawl Timeline

Artifacts Archive

Docs & README

🏗️ RAG Article App — Architecture & Workflow

📐 High-Level Architecture

🔄 Detailed Flow — User Search

📰 User Browse Flow (No Search)

🤖 CrewAI Agents

Web Search Agent

Summary Agent

🗄️ Data Schema

Firebase (Firestore) — articles collection

BigQuery — articles table

💰 Cost Estimate (POC)

🚀 Deployment

🔮 Future: Production Migration

Contract & API

Reliability & Benchmarks

Media & Demo

Related Agents

@x1pay/langchain

@langchain/langgraph-swarm

@langchain/langgraph-supervisor

oceanbus-langchain

Firebase (Firestore) — `articles` collection

BigQuery — `articles` table