Xpersona

Crawler Summary

CrewAI_Engineering_Team answer-first brief

A resumable, rate-limited, multi-model CrewAI workflow that coordinates design, backend, frontend, and testing agents to build a trading simulation account manager. It generates a Python backend, Gradio dashboard, and tests for deposits, withdrawals, share trading, portfolios, P&L, and transaction history. πŸ€– CrewAI Engineering Team A **multi-agent software engineering workflow built with CrewAI** that simulates an AI engineering team capable of designing, implementing, testing, and presenting a complete application. The project coordinates specialized AI agents across multiple development stages to build a **trading simulation account manager** with a Python backend, Gradio interface, and automated tests. Instead of r Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

CrewAI_Engineering_Team 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
Claim this agent
Agent DossierGITHUB REPOSSafety: 66/100

CrewAI_Engineering_Team

A resumable, rate-limited, multi-model CrewAI workflow that coordinates design, backend, frontend, and testing agents to build a trading simulation account manager. It generates a Python backend, Gradio dashboard, and tests for deposits, withdrawals, share trading, portfolios, P&L, and transaction history. πŸ€– CrewAI Engineering Team A **multi-agent software engineering workflow built with CrewAI** that simulates an AI engineering team capable of designing, implementing, testing, and presenting a complete application. The project coordinates specialized AI agents across multiple development stages to build a **trading simulation account manager** with a Python backend, Gradio interface, and automated tests. Instead of r

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

Saksham5k2

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Unpublished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

Saksham5k2

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 Β· 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

text

.crewai-data/engineering_team_run_state.json

text

Engineering Lead  β†’ Model A
Backend Engineer  β†’ Model B
Frontend Engineer β†’ Model C
Test Engineer     β†’ Model D

text

sandbox/
β”‚
β”œβ”€β”€ account.py
β”œβ”€β”€ app.py
└── test_account.py

text

CrewAI_Engineering_Team/
β”‚
β”œβ”€β”€ src/
β”‚   └── engineering_team/
β”‚       β”œβ”€β”€ config/
β”‚       β”‚   β”œβ”€β”€ agents.yaml
β”‚       β”‚   └── tasks.yaml
β”‚       β”‚
β”‚       β”œβ”€β”€ crew.py
β”‚       β”œβ”€β”€ main.py
β”‚       └── ...
β”‚
β”œβ”€β”€ sandbox/
β”‚   β”œβ”€β”€ account.py
β”‚   β”œβ”€β”€ app.py
β”‚   └── test_account.py
β”‚
β”œβ”€β”€ .crewai-data/
β”‚   └── engineering_team_run_state.json
β”‚
β”œβ”€β”€ .env
β”œβ”€β”€ pyproject.toml
β”œβ”€β”€ uv.lock
└── README.md

bash

git clone https://github.com/saksham5k2/CrewAI_Engineering_Team.git
cd CrewAI_Engineering_Team

bash

uv sync

Docs & README

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

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

A resumable, rate-limited, multi-model CrewAI workflow that coordinates design, backend, frontend, and testing agents to build a trading simulation account manager. It generates a Python backend, Gradio dashboard, and tests for deposits, withdrawals, share trading, portfolios, P&L, and transaction history. πŸ€– CrewAI Engineering Team A **multi-agent software engineering workflow built with CrewAI** that simulates an AI engineering team capable of designing, implementing, testing, and presenting a complete application. The project coordinates specialized AI agents across multiple development stages to build a **trading simulation account manager** with a Python backend, Gradio interface, and automated tests. Instead of r

Full README

πŸ€– CrewAI Engineering Team

A multi-agent software engineering workflow built with CrewAI that simulates an AI engineering team capable of designing, implementing, testing, and presenting a complete application.

The project coordinates specialized AI agents across multiple development stages to build a trading simulation account manager with a Python backend, Gradio interface, and automated tests.

Instead of relying on a single LLM call to generate an entire application, the system separates the development process into specialized engineering roles and lets agents collaborate through a structured workflow.

✨ What This Project Demonstrates

This project explores how agentic AI can be used for software engineering workflows.

The Crew acts like a small engineering team:

Requirements > System Design > Backend Development > Frontend Development > Testing > Final Application

Each stage receives relevant context from the previous stage and focuses on a specific engineering responsibility.

🧠 Agent Workflow

The project follows a four-stage development pipeline.

1. Engineering Lead

Analyzes the application requirements and produces a concise backend design covering:

  • account structure
  • transaction structure
  • method interfaces
  • validation rules
  • share-price interface
  • expected system behavior

The goal is to establish a clear contract before implementation begins.

2. Backend Engineer

Implements the core trading-account logic in Python.

Responsibilities include:

  • account creation
  • deposits
  • withdrawals
  • cash balance management
  • buying shares
  • selling shares
  • portfolio tracking
  • transaction history
  • profit and loss calculations
  • validation and error handling

3. Frontend Engineer

Builds a lightweight Gradio dashboard on top of the generated backend.

The interface allows users to interact with the trading simulation without directly calling Python methods.

4. Test Engineer

Creates automated tests for the generated backend using Python's standard testing tools.

The tests validate important business rules and help verify that the implementation behaves as expected.

πŸš€ Key Features

  • πŸ€– Multi-agent development workflow with CrewAI
  • 🧩 Specialized engineering roles
  • 🧠 Context sharing between agents
  • πŸ” Resumable execution using checkpoints
  • ⏱️ Built-in Groq API rate-limit handling
  • πŸ”€ Optional multi-model agent configuration
  • 🐍 Generated Python backend
  • 🎨 Generated Gradio dashboard
  • πŸ§ͺ Automated unit-test generation
  • πŸ“ Isolated generated-code sandbox
  • πŸ’° Trading and portfolio simulation
  • πŸ“Š Holdings, balance and P&L tracking
  • 🧾 Transaction history

πŸ—οΈ Trading Application

The generated application acts as a simplified investment account manager.

Users can:

  • create an account
  • deposit funds
  • withdraw available cash
  • buy shares
  • sell owned shares
  • inspect current holdings
  • calculate portfolio value
  • inspect profit and loss
  • review transaction history

The project is intended as an AI engineering workflow demonstration, not as a real trading or financial platform.

πŸ› οΈ Tech Stack

| Component | Technology | | --------------------- | -------------------------- | | Agent Framework | CrewAI | | Language | Python | | LLM Provider | Groq | | Models | Configurable / Multi-model | | Frontend | Gradio | | Dependency Management | uv | | Testing | Python standard library | | Configuration | YAML | | Environment Variables | .env |

πŸ”„ Resumable Agent Execution

LLM workflows can fail because of:

  • API rate limits
  • temporary network failures
  • provider errors
  • long-running multi-agent executions

Restarting the entire Crew after every failure wastes both tokens and time.

This project therefore maintains execution state between stages.

Completed stages are checkpointed in:

.crewai-data/engineering_team_run_state.json

If execution stops after a completed stage, running the Crew again can continue from the remaining work instead of rebuilding everything from scratch.

Generated files inside sandbox/ are also preserved between resumed runs.

⏱️ Rate-Limit Management

Groq provides very fast inference, but API usage is still governed by limits such as Tokens Per Minute (TPM) and Tokens Per Day (TPD).

This project serializes LLM requests and introduces controlled cooldowns between expensive calls.

The workflow is therefore designed to trade a small amount of execution speed for improved reliability during longer multi-agent runs.

This becomes especially useful when several agents are sharing the same provider quota.

πŸ”€ Multi-Model Support

Although the workflow can run entirely through Groq, the architecture also supports assigning different models or providers to different engineering roles.

For example:

Engineering Lead  β†’ Model A
Backend Engineer  β†’ Model B
Frontend Engineer β†’ Model C
Test Engineer     β†’ Model D

This makes it possible to experiment with model specialization rather than assuming one model is optimal for every software-engineering task.

A frontend-focused model, for example, can be assigned to UI generation while another model handles backend reasoning.

πŸ“Š UI Results

The below images are the outcome from the frontier models like GPT-5.6-SOL-HIGH AND CLAUDE-SONNET-5

<img width="651" height="593" alt="1" src="https://github.com/user-attachments/assets/956d5968-a447-4a09-8d98-d0def30e04c6" /> <img width="650" height="646" alt="2" src="https://github.com/user-attachments/assets/2b949d53-1dd4-4015-a2c4-9dd2368d9c8b" /> <img width="751" height="698" alt="3" src="https://github.com/user-attachments/assets/ff5603b0-4621-4bdd-95f2-f95b94e2b36e" /> <img width="653" height="209" alt="4" src="https://github.com/user-attachments/assets/89666555-47ae-42e3-af13-7f9fe97daf1c" />

The below images are the outcome from GPT-OSS-120b model.

<img width="1246" height="527" alt="A" src="https://github.com/user-attachments/assets/08fdc58c-794b-4ae4-93f8-7606cb29bae2" /> <img width="1190" height="497" alt="B" src="https://github.com/user-attachments/assets/040ba9fc-17b1-4273-951c-2675c4a27c86" />

πŸ“‚ Generated Output

The Crew writes generated application files into the sandbox/ directory.

Typical output:

sandbox/
β”‚
β”œβ”€β”€ account.py
β”œβ”€β”€ app.py
└── test_account.py

account.py

Contains the generated domain model and trading logic.

app.py

Contains the generated Gradio application.

test_account.py

Contains the generated unit tests.

Keeping generated code inside a sandbox separates agent orchestration code from agent-produced application code.

πŸ“ Project Structure

A complete local project follows this structure:

CrewAI_Engineering_Team/
β”‚
β”œβ”€β”€ src/
β”‚   └── engineering_team/
β”‚       β”œβ”€β”€ config/
β”‚       β”‚   β”œβ”€β”€ agents.yaml
β”‚       β”‚   └── tasks.yaml
β”‚       β”‚
β”‚       β”œβ”€β”€ crew.py
β”‚       β”œβ”€β”€ main.py
β”‚       └── ...
β”‚
β”œβ”€β”€ sandbox/
β”‚   β”œβ”€β”€ account.py
β”‚   β”œβ”€β”€ app.py
β”‚   └── test_account.py
β”‚
β”œβ”€β”€ .crewai-data/
β”‚   └── engineering_team_run_state.json
β”‚
β”œβ”€β”€ .env
β”œβ”€β”€ pyproject.toml
β”œβ”€β”€ uv.lock
└── README.md

βš™οΈ Prerequisites

Make sure you have:

  • Python 3.10–3.13
  • uv
  • a Groq API key
  • Additional provider API keys are only required when experimenting with multi-model configurations.

πŸ“¦ Installation

Clone the repository:

git clone https://github.com/saksham5k2/CrewAI_Engineering_Team.git
cd CrewAI_Engineering_Team

Install the locked dependencies:

uv sync

πŸ”‘ Environment Setup

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key

When using additional providers, add their required API keys as well.

Never commit API keys or your .env file to GitHub.

▢️ Run the Engineering Crew

From the repository root:

crewai run

The agents will execute their assigned engineering tasks sequentially.

Generated application files will be written into:

sandbox/

πŸ” Start a Completely Fresh Run

Normal execution preserves completed stages and existing sandbox output.

If you intentionally want to discard previous state and regenerate everything:

Linux / macOS

CREWAI_FRESH_RUN=1 crewai run

PowerShell

$env:CREWAI_FRESH_RUN="1"
crewai run

This clears the previous execution state and starts the engineering workflow again.

πŸ–₯️ Launch the Generated Application

After the Crew completes successfully:

cd sandbox

Then launch the generated Gradio interface:

uv run python -c "import app; app.demo.launch()"

Gradio will print a local URL similar to:

http://127.0.0.1:7860

Open the URL in your browser.

Stop the application with:

Ctrl + C

πŸ§ͺ Run the Generated Tests

From inside the sandbox:

python -m unittest test_account.py

The generated tests validate the trading-account implementation produced by the engineering agents.

πŸ’‘ Why Multi-Agent Software Engineering?

Traditional LLM code generation often looks like:

Prompt β†’ LLM β†’ Entire Application

That approach forces one context to handle architecture, implementation, UI, and testing simultaneously.

This project instead experiments with:

Requirements
    ↓
Architecture
    ↓
Backend
    ↓
Frontend
    ↓
Testing
    ↓
Application

The separation provides clearer responsibilities and makes it easier to experiment with:

  • agent specialization
  • task decomposition
  • model specialization
  • context management
  • failure recovery
  • automated validation

The project therefore focuses as much on AI workflow engineering as on the application produced by the agents.

🎯 Project Goals

The main objective is not simply to generate a trading dashboard.

The trading application acts as a test case for exploring how autonomous agents can collaborate across a software development lifecycle.

The project demonstrates practical concepts including:

  • Agentic AI
  • Multi-agent orchestration
  • LLM-based software engineering
  • Structured task delegation
  • Model routing
  • Rate-limit-aware workflows
  • Stateful execution
  • Automated code generation
  • AI-generated testing

⚠️ Disclaimer

This project creates a simulated trading application for educational and experimental purposes.

It does not connect to a brokerage, execute real financial transactions, or provide financial advice.

πŸ‘¨β€πŸ’» Author

Saksham

Building practical projects around LLMs, RAG, AI agents, model fine-tuning, and AI engineering.

GitHub: @saksham5k2

⭐ Support

If you find this project useful or interesting, consider giving the repository a ⭐.

It helps support further experiments in agentic AI and LLM engineering.

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-saksham5k2-crewai-engineering-team/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/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-saksham5k2-crewai-engineering-team/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T15:37:00.728Z"
    }
  },
  "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": "Saksham5k2",
    "href": "https://github.com/saksham5k2/CrewAI_Engineering_Team",
    "sourceUrl": "https://github.com/saksham5k2/CrewAI_Engineering_Team",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T13:50:59.861Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T13:50:59.861Z",
    "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-saksham5k2-crewai-engineering-team/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-saksham5k2-crewai-engineering-team/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub Β· 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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