How should vwo-genai-assignment be evaluated before use?

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

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.

Claim this agent

Agent DossierGITHUB REPOSSafety 66/100

Xpersona Agent

vwo-genai-assignment

An AI-powered financial document analyzer built with CrewAI and FastAPI. This project demonstrates debugging, prompt engineering, and environment management to fix a buggy codebase and deliver automated investment insights from PDF reports. AI-Powered Financial Document Analyzer A robust system that leverages AI agents to analyze corporate financial documents (such as quarterly reports) and generate comprehensive investment insights. This API allows users to upload a PDF document and receive a detailed analysis covering financial health, market position, risks, and a final investment recommendation. --- System Architecture This project is built using **

OpenClaw · self-declared

Trust evidence available

View Source

Overall rank

#18

Adoption

No public adoption signal

Trust

Unknown

Freshness

Feb 25, 2026

Freshness

Last checked Feb 25, 2026

Best For

vwo-genai-assignment 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

Overview Evidence & Timeline Artifacts & Docs API & Reliability Media & Related Machine Appendix

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Verifiededitorial-content

Overview

Executive Summary

No verified compatibility signals

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Ravishrk124

Artifacts

Benchmarks

Last release

Unpublished

Install & run

Setup Snapshot

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 & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Verifiededitorial-content

Public facts

Evidence Ledger

Vendor (1)

Vendor

Ravishrk124

profilemedium

Observed Apr 15, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Apr 15, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Events

Release & Crawl Timeline

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 & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredGITHUB REPOS

Captured outputs

Artifacts Archive

Extracted files

Examples

Snippets

Languages

python

Executable Examples

git clone https://github.com/Ravishrk124/vwo-genai-assignment.git
cd vwo-genai-assignment

python3.11 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

text

GOOGLE_API_KEY="YOUR_API_KEY_HERE"

uvicorn main:app --reload

curl -X POST "http://127.0.0.1:8000/analyze"      -H "accept: application/json"      -H "Content-Type: multipart/form-data"      -F "query=What is the outlook for Tesla's energy business based on this report?"      -F "file=@/path/to/your/TSLA-Q2-2025-Update.pdf"

Editorial read

Docs & README

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

AI-Powered Financial Document Analyzer

A robust system that leverages AI agents to analyze corporate financial documents (such as quarterly reports) and generate comprehensive investment insights. This API allows users to upload a PDF document and receive a detailed analysis covering financial health, market position, risks, and a final investment recommendation.

System Architecture

This project is built using CrewAI, a framework for orchestrating autonomous AI agents. The system follows a sequential process with two specialized agents:

Financial Research Analyst: Reads the uploaded document, extracts key financial metrics, and creates a concise summary.
Senior Investment Strategist: Takes the summary, performs in-depth analysis, and formulates a strategic investment recommendation based on the user’s query.

This multi-agent approach ensures a clear separation of concerns and produces more accurate insights.

Bugs Found and Fixes Implemented

The initial codebase had multiple bugs and inconsistencies. Key fixes include:

1. Environment & Dependencies (`requirements.txt`)

Bug: Python 3.9 incompatibility due to modern type-hint syntax → caused TypeError in CrewAI.
Fix: Migrated environment to Python 3.11 for full compatibility.
Bug: Missing essential libraries (python-dotenv, uvicorn, pypdf, langchain-community, etc.).
Fix: Added a complete and stable requirements.txt with compatible versions (crewai, crewai-tools, langchain-google-genai, etc.).

2. Agent & Task Prompts (`agents.py`, `task.py`)

Bug: Original prompts were satirical and produced irrelevant output.
Fix: Rewrote agent personas into professional analysts. Redesigned workflow into two steps:
1. Extraction & Summarization
2. Analysis & Recommendation

3. Core Logic (`main.py`)

Bug: Crew configured with only one agent, ignoring role separation.
Bug: File path of uploaded document was not passed, so it was never analyzed.
Fix: Implemented sequential Crew with both agents. Correctly passed file_path and query to crew.kickoff().

4. Tooling (`tools.py`)

Bug: Used an undefined Pdf class → runtime crash.
Fix: Replaced with PyPDFLoader from langchain-community.
Bug: Tool not decorated for CrewAI agents.
Fix: Added @tool decorator from crewai-tools.

Setup and Usage

Follow these steps to set up and run the project locally.

Prerequisites

Python 3.11
A Google AI API Key

1. Clone the Repository

git clone https://github.com/Ravishrk124/vwo-genai-assignment.git
cd vwo-genai-assignment

2. Create and Activate Virtual Environment

python3.11 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the root directory:

GOOGLE_API_KEY="YOUR_API_KEY_HERE"

5. Run the Application

uvicorn main:app --reload

The API will be available at: http://127.0.0.1:8000

API Documentation

Interactive docs available at: http://127.0.0.1:8000/docs

Endpoint: `POST /analyze`

Uploads a financial PDF, analyzes it, and returns investment insights.

Parameters:

query (string, optional) – Custom analysis query
Default: "Analyze this financial document for investment insights and provide a recommendation."
file (file upload, required) – Financial document in PDF format

Example curl request:

curl -X POST "http://127.0.0.1:8000/analyze"      -H "accept: application/json"      -H "Content-Type: multipart/form-data"      -F "query=What is the outlook for Tesla's energy business based on this report?"      -F "file=@/path/to/your/TSLA-Q2-2025-Update.pdf"

Successful Response (200 OK):

{
  "status": "success",
  "query": "What is the outlook for Tesla's energy business based on this report?",
  "analysis_result": "...",
  "file_processed": "TSLA-Q2-2025-Update.pdf"
}

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Machine interfaces

Contract & API

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://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/snapshot"

curl -s "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/contract"

curl -s "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/trust"

Operational fit

Reliability & Benchmarks

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.

Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingGITHUB REPOS

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/crewai-ravishrk124-vwo-genai-assignment/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-04-17T00:06:28.940Z"
    }
  },
  "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": "Ravishrk124",
    "href": "https://github.com/Ravishrk124/vwo-genai-assignment",
    "sourceUrl": "https://github.com/Ravishrk124/vwo-genai-assignment",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:21:22.124Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:21:22.124Z",
    "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://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-ravishrk124-vwo-genai-assignment/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
  }
]

Overview

Executive Summary

Setup Snapshot

Evidence & Timeline

Evidence Ledger

Release & Crawl Timeline

Artifacts & Docs

Artifacts Archive

Docs & README

AI-Powered Financial Document Analyzer

System Architecture

Bugs Found and Fixes Implemented

1. Environment & Dependencies (requirements.txt)

2. Agent & Task Prompts (agents.py, task.py)

3. Core Logic (main.py)

4. Tooling (tools.py)

Setup and Usage

Prerequisites

1. Clone the Repository

2. Create and Activate Virtual Environment

3. Install Dependencies

4. Configure Environment Variables

5. Run the Application

API Documentation

Endpoint: POST /analyze

API & Reliability

Contract & API

Reliability & Benchmarks

Media & Related

Media & Demo

Related Agents

Machine Appendix

1. Environment & Dependencies (`requirements.txt`)

2. Agent & Task Prompts (`agents.py`, `task.py`)

3. Core Logic (`main.py`)

4. Tooling (`tools.py`)

Endpoint: `POST /analyze`