Xpersona

Crawler Summary

Real-estate-market-intelligence-agent answer-first brief

CrewAI 3-agent property valuation system — comps analysis, market trends, investor report. GPT-4o · LangSmith · GCP Cloud Run. Real Estate Market Intelligence Agent Business Problem Real estate analysts spend hours manually pulling comparable sales, market trend data, and neighbourhood risk scores to produce property valuation reports. A multi-agent system can automate the full pipeline — pulling comps, analysing market trends, scoring risk, and drafting investor-ready reports in minutes. Project Objective Build a CrewAI 3-agent pipeline: - Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

Real-estate-market-intelligence-agent 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 REPOS, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Agent DossierGITHUB REPOSSafety: 66/100

Real-estate-market-intelligence-agent

CrewAI 3-agent property valuation system — comps analysis, market trends, investor report. GPT-4o · LangSmith · GCP Cloud Run. Real Estate Market Intelligence Agent Business Problem Real estate analysts spend hours manually pulling comparable sales, market trend data, and neighbourhood risk scores to produce property valuation reports. A multi-agent system can automate the full pipeline — pulling comps, analysing market trends, scoring risk, and drafting investor-ready reports in minutes. Project Objective Build a CrewAI 3-agent pipeline: -

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Apuroopy1 Prog

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Unpublished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 10/9/2026.

View Source

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

Apuroopy1 Prog

profilemedium
Observed Oct 9, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Oct 9, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

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

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

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

Self-declaredGITHUB REPOS

Extracted files

0

Examples

6

Snippets

0

Languages

python

Executable Examples

mermaid

graph TD
    A[Property Input JSON] --> B[FastAPI trigger]
    B --> C[CrewAI Crew - 3 agents]
    C --> D[Agent 1: Comps Analyst]
    D --> E[Agent 2: Market Trend Analyst]
    E --> F[Agent 3: Valuation Report Writer]
    F --> G[Structured Report + Recommendation]
    C --> H[LangSmith - trace all steps]
    B --> I[GCP Cloud Run deployment]

text

project-01-real-estate-market-intelligence-agent/
├── app/
│   ├── agents.py           # CrewAI agent definitions
│   ├── tasks.py            # Task definitions with Instructor validation
│   ├── crew.py             # Crew orchestration
│   └── api.py              # FastAPI handler
├── utils/
│   └── cost_tracker.py     # Token budget enforcer
├── evaluation/
│   ├── langsmith_eval.py   # LangSmith eval suite
│   └── test_cases.json     # 10 property test scenarios
├── infra/
│   ├── Dockerfile
│   ├── service.yaml        # GCP Cloud Run service config
│   └── cloudbuild.yaml
├── guardrails/
│   └── __init__.py
├── tests/
│   └── test_agents.py
├── samples/
│   └── sample_property.json
├── langsmith_config.py
├── .github/workflows/deploy.yml
├── .env.example
├── requirements.txt
└── README.md

bash

pip install -r requirements.txt
cp .env.example .env
uvicorn app.api:app --reload

# Deploy to GCP Cloud Run
gcloud builds submit --config infra/cloudbuild.yaml

bash

mkdir project-01-real-estate-market-intelligence-agent
cd project-01-real-estate-market-intelligence-agent
python -m venv venv && source venv/bin/activate
mkdir -p app utils evaluation infra tests samples guardrails

bash

pip install -r requirements.txt

text

Property input (address, beds, baths, sqft, asking price)
       ↓
Agent 1: Comps Analyst        ← finds 5 comparable recent sales, scores similarity
       ↓
Agent 2: Market Trend Analyst ← analyses 90-day price trend, DOM, absorption rate
       ↓
Agent 3: Valuation Report Writer ← produces final valuation with confidence band + recommendation
       ↓
FastAPI response + LangSmith trace

Docs & README

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

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

CrewAI 3-agent property valuation system — comps analysis, market trends, investor report. GPT-4o · LangSmith · GCP Cloud Run. Real Estate Market Intelligence Agent Business Problem Real estate analysts spend hours manually pulling comparable sales, market trend data, and neighbourhood risk scores to produce property valuation reports. A multi-agent system can automate the full pipeline — pulling comps, analysing market trends, scoring risk, and drafting investor-ready reports in minutes. Project Objective Build a CrewAI 3-agent pipeline: -

Full README

Real Estate Market Intelligence Agent

Level Industry Stack

Business Problem

Real estate analysts spend hours manually pulling comparable sales, market trend data, and neighbourhood risk scores to produce property valuation reports. A multi-agent system can automate the full pipeline — pulling comps, analysing market trends, scoring risk, and drafting investor-ready reports in minutes.

Project Objective

Build a CrewAI 3-agent pipeline:

  • Agent 1 — Comps Analyst: searches and scores comparable property sales in the target area
  • Agent 2 — Market Trend Analyst: analyses price trends, days-on-market, supply/demand signals
  • Agent 3 — Valuation Report Writer: combines comps + trends into a structured valuation report with buy/sell/hold recommendation

Deployed on GCP Cloud Run. All agent runs traced in LangSmith.

System Architecture

graph TD
    A[Property Input JSON] --> B[FastAPI trigger]
    B --> C[CrewAI Crew - 3 agents]
    C --> D[Agent 1: Comps Analyst]
    D --> E[Agent 2: Market Trend Analyst]
    E --> F[Agent 3: Valuation Report Writer]
    F --> G[Structured Report + Recommendation]
    C --> H[LangSmith - trace all steps]
    B --> I[GCP Cloud Run deployment]

Folder Structure

project-01-real-estate-market-intelligence-agent/
├── app/
│   ├── agents.py           # CrewAI agent definitions
│   ├── tasks.py            # Task definitions with Instructor validation
│   ├── crew.py             # Crew orchestration
│   └── api.py              # FastAPI handler
├── utils/
│   └── cost_tracker.py     # Token budget enforcer
├── evaluation/
│   ├── langsmith_eval.py   # LangSmith eval suite
│   └── test_cases.json     # 10 property test scenarios
├── infra/
│   ├── Dockerfile
│   ├── service.yaml        # GCP Cloud Run service config
│   └── cloudbuild.yaml
├── guardrails/
│   └── __init__.py
├── tests/
│   └── test_agents.py
├── samples/
│   └── sample_property.json
├── langsmith_config.py
├── .github/workflows/deploy.yml
├── .env.example
├── requirements.txt
└── README.md

Setup

pip install -r requirements.txt
cp .env.example .env
uvicorn app.api:app --reload

# Deploy to GCP Cloud Run
gcloud builds submit --config infra/cloudbuild.yaml

Observability

  • LangSmith: every CrewAI agent step is traced. View at smith.langchain.com
  • Cost tracking: TokenBudget enforces 80K token max per crew run
  • Set LANGCHAIN_TRACING_V2=true and LANGCHAIN_API_KEY in .env

Key Concepts

  • CrewAI multi-agent orchestration with role/goal/backstory
  • Instructor-validated Pydantic output schemas per agent
  • GCP Cloud Run serverless deployment with cloudbuild CI/CD
  • LangSmith tracing for multi-agent observability

Interview Talking Points

  1. How do agents pass context between each other in CrewAI?
  2. How would you handle real-time MLS data integration?
  3. What guardrails prevent hallucinated comps or fake valuations?
  4. How would you evaluate valuation accuracy at scale?
  5. Why Cloud Run over Cloud Functions for this workload?

Time Estimate

| Mode | Time | |---|---| | Self-paced | 18–24 hours | | Instructor-guided | 10–14 hours |

Step-by-Step Implementation Guide

Step 1: Project Setup

mkdir project-01-real-estate-market-intelligence-agent
cd project-01-real-estate-market-intelligence-agent
python -m venv venv && source venv/bin/activate
mkdir -p app utils evaluation infra tests samples guardrails

Install dependencies:

pip install -r requirements.txt

Step 2: Understand the Multi-Agent Architecture

Property input (address, beds, baths, sqft, asking price)
       ↓
Agent 1: Comps Analyst        ← finds 5 comparable recent sales, scores similarity
       ↓
Agent 2: Market Trend Analyst ← analyses 90-day price trend, DOM, absorption rate
       ↓
Agent 3: Valuation Report Writer ← produces final valuation with confidence band + recommendation
       ↓
FastAPI response + LangSmith trace

Step 3: Build the Agents (app/agents.py)

See app/agents.py for full implementation.

Step 4: Define Tasks with Instructor Validation (app/tasks.py)

See app/tasks.py for full implementation.

Step 5: Wire the Crew (app/crew.py)

See app/crew.py for full implementation.

Step 6: Expose via FastAPI (app/api.py)

See app/api.py for full implementation.

Step 7: Add Cost Tracking (utils/cost_tracker.py)

See utils/cost_tracker.py for full implementation.

Step 8: Deploy to GCP Cloud Run

gcloud auth login
gcloud config set project YOUR_PROJECT_ID
gcloud builds submit --config infra/cloudbuild.yaml

Contract & API

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

MissingGITHUB REPOS

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

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-apuroopy1-prog-real-estate-market-intelligence-agent/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/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.

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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-apuroopy1-prog-real-estate-market-intelligence-agent/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-10T01:00:43.027Z"
    }
  },
  "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",
    "category": "vendor",
    "label": "Vendor",
    "value": "Apuroopy1 Prog",
    "href": "https://github.com/apuroopy1-prog/Real-estate-market-intelligence-agent",
    "sourceUrl": "https://github.com/apuroopy1-prog/Real-estate-market-intelligence-agent",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T23:22:34.955Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T23:22:34.955Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-apuroopy1-prog-real-estate-market-intelligence-agent/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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