How should AI-Agents-From-Idea-to-Deployment 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

AI-Agents-From-Idea-to-Deployment answer-first brief

A hands-on workshop template that demonstrates how to orchestrate CrewAI agents for planning, research, writing, and review workflows. The stack combines CrewAI with LangChain tools, a FAISS-backed Retrieval-Augmented Generation (RAG) pipeline, and a Streamlit frontend. Agentic AI Workshop: Multi-Agent Systems From Idea to Deployment A hands-on workshop template that demonstrates how to orchestrate CrewAI agents for planning, research, writing, and review workflows. The stack combines CrewAI with LangChain tools, a FAISS-backed Retrieval-Augmented Generation (RAG) pipeline, and a Streamlit frontend. All large language model calls are routed through the OpenRouter API using the model Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

AI-Agents-From-Idea-to-Deployment 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

Agent DossierGitHubSafety: 66/100

AI-Agents-From-Idea-to-Deployment

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals3 GitHub stars

Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 10/9/2026.

3 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Awais Asghar

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Unpublished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. 3 GitHub stars reported by the source. Last updated 10/9/2026.

View Source

Setup snapshot

git clone https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment.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

Awais Asghar

profilemedium

Observed Apr 16, 2026Source link Provenance

Compatibility (2)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Protocol compatibility

OpenClaw

contractmedium

Observed Apr 16, 2026Source link Provenance

Adoption (2)

Adoption signal

3 GitHub stars

profilemedium

Observed Oct 9, 2026Source link Provenance

Adoption signal

2 GitHub stars

profilemedium

Observed Apr 16, 2026Source link Provenance

Security (2)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

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

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 OPENCLEW

Extracted files

Examples

Snippets

Languages

python

Executable Examples

text

agentic-workshop/
├── .env.example
├── requirements.txt
├── README.md
├── main.py
├── crew.py
├── tasks.py
├── config/
│   └── settings.py
├── agents/
│   ├── __init__.py
│   ├── planner.py
│   ├── researcher.py
│   ├── writer.py
│   └── reviewer.py
├── tools/
│   ├── __init__.py
│   ├── rag_tool.py
│   ├── web_search.py
│   └── calculator.py
├── rag/
│   ├── build_vector_db.py
│   ├── documents/
│   │   └── sample_docs.txt
│   └── vectorstore/
└── frontend/
   └── app.py

powershell

git clone https://github.com/your-org/agentic-workshop.git
   cd agentic-workshop

powershell

python -m venv .venv
   .\.venv\Scripts\Activate.ps1

powershell

pip install -r requirements.txt

powershell

copy .env.example .env
   # Edit .env and paste your actual OpenRouter API key

powershell

python rag\build_vector_db.py

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

Agentic AI Workshop: Multi-Agent Systems From Idea to Deployment

Workshop Goals

Teach students how to structure multi-agent systems with CrewAI.
Illustrate how RAG augments agents with curated context via FAISS.
Showcase live web search and deterministic calculation tooling.
Provide an end-to-end example from initial idea to reviewed deliverable.
Offer a Streamlit interface that makes the pipeline demo-ready for classes and talks.

Project Structure

agentic-workshop/
├── .env.example
├── requirements.txt
├── README.md
├── main.py
├── crew.py
├── tasks.py
├── config/
│   └── settings.py
├── agents/
│   ├── __init__.py
│   ├── planner.py
│   ├── researcher.py
│   ├── writer.py
│   └── reviewer.py
├── tools/
│   ├── __init__.py
│   ├── rag_tool.py
│   ├── web_search.py
│   └── calculator.py
├── rag/
│   ├── build_vector_db.py
│   ├── documents/
│   │   └── sample_docs.txt
│   └── vectorstore/
└── frontend/
   └── app.py

Built-in Agent Tooling

Every agent in the crew (planner, researcher, writer, reviewer) receives the same trio of tools via tools.get_default_toolkit():

local_rag_search: FAISS-backed retrieval over curated workshop documents for grounded answers.
duckduckgo_search: Live DuckDuckGo lookups when the topic needs current context or external validation.
calculator: A deterministic evaluator for quick math, metrics, or cost estimates referenced in drafts.

Having the shared toolkit means any role can validate facts or pull references without delegating to the researcher.

Prerequisites

Python 3.10+
An OpenRouter account and API key (free tier available)
(Optional) A virtual environment manager such as venv, conda, or pipenv

Installation

Clone the repository

git clone https://github.com/your-org/agentic-workshop.git
cd agentic-workshop

Create and activate a virtual environment

python -m venv .venv
.\.venv\Scripts\Activate.ps1

Install project dependencies
```
pip install -r requirements.txt
```

Set up environment variables

copy .env.example .env
# Edit .env and paste your actual OpenRouter API key

Build the FAISS vector store (one-time setup)
```
python rag\build_vector_db.py
```

Running the Backend Pipeline

Execute the crew directly from the command line:

python main.py --topic "Agentic AI Workshop on Robotics Deployments"

The script loads environment variables, constructs the CrewAI workflow, and prints the reviewed deliverable to stdout.

Using `run_pipeline` Programmatically

Import run_pipeline from main.py to embed the workflow inside other applications:

from main import run_pipeline

result = run_pipeline("Multi-Agent Workshop for Healthcare AI")
print(result)

Running the Streamlit Frontend

Launch the UI from the virtual environment so Streamlit can resolve the backend packages:

python -m streamlit run frontend\app.py

Enter a workshop topic in the sidebar and click Run Pipeline. The output panel displays the aggregated crew result when the run completes.

If you prefer to call the executable directly on Windows, use .\.venv\Scripts\streamlit.exe run frontend\app.py from the activated environment.

Customising Agents and Tasks

Agent Prompts: Update the placeholder system prompts in agents/planner.py, agents/researcher.py, agents/writer.py, and agents/reviewer.py to align with your scenario.
Task Objectives: Adjust the descriptions and expected outputs in tasks.py to fit new deliverables or grading rubrics.
Tools: Extend tools/ with new integrations (e.g., GitHub search, deployment triggers) and register them in tools/__init__.py plus the relevant tasks.
LLM Settings: Tweak config/settings.py to experiment with temperatures, token limits, or alternative OpenRouter models.
Knowledge Base: Replace rag/documents/sample_docs.txt with your own corpus and re-run python rag\build_vector_db.py.

Deploying the System

Streamlit Community Cloud: Upload the repo, set environment variables (OPENROUTER_API_KEY, optional fallbacks) in the project settings, and ensure requirements.txt is listed as the sole dependency file.
Containerised App: Package the CLI and Streamlit UI inside a Docker image (start from python:3.11-slim, copy the repo, install requirements, expose port 8501). Deploy to Azure App Service, AWS App Runner, or Google Cloud Run.
API Gateway: Wrap run_workshop_pipeline with FastAPI or Flask to expose a /run endpoint, then host behind a queue/worker on ECS, Azure Container Apps, or Fly.io for managed execution.
Enterprise Integration: For internal workshops, schedule the pipeline via orchestration tools (Airflow, Prefect) and archive outputs to cloud storage, allowing instructors to diff successive runs.

Troubleshooting Tips

Missing Vector Store: If the research task fails to load the FAISS index, ensure rag/vectorstore/ contains the generated files. Re-run the build script if needed.
Authentication Errors: Double-check that OPENROUTER_API_KEY is present in your environment. The app raises an explicit error if it is missing.
Dependency Issues: Match the Python version requirement and reinstall with pip install --upgrade -r requirements.txt when packages change.

Next Steps for Students

Each student in a group should take an agent and then write its prompt.

Try to develop a simple crew AI chain for any basic task. (Like Research, Newsroom, Study Companion and sky is the limit)
Add or Remove an Agent
Try to make a new tool, like drawing maker. (Hint use canvas and LLM written code to draw lines on it)
Try Deploying
Play around with Prompts Run the pipeline ;)

Happy building! Customize freely to turn this template into a polished workshop experience.

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-awais-asghar-ai-agents-from-idea-to-deployment/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/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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MCPOPENCLAW

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activepieces

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MCPOPENCLAW

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The Frontend for Agents & Generative UI. React + Angular

OPENCLAW

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-awais-asghar-ai-agents-from-idea-to-deployment/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-09T21:44:34.136Z"
    }
  },
  "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": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T07:42:24.483Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "3 GitHub stars",
    "href": "https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment",
    "sourceUrl": "https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T07:42:24.483Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Awais Asghar",
    "category": "vendor",
    "href": "https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment",
    "sourceUrl": "https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-16T06:46:54.175Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-16T06:46:54.175Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "traction",
    "label": "Adoption signal",
    "value": "2 GitHub stars",
    "category": "adoption",
    "href": "https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment",
    "sourceUrl": "https://github.com/Awais-Asghar/AI-Agents-From-Idea-to-Deployment",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-16T06:46:54.175Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "docs_crawl",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "category": "integration",
    "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,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-awais-asghar-ai-agents-from-idea-to-deployment/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,
    "metadata": {}
  }
]

AI-Agents-From-Idea-to-Deployment answer-first brief

Executive Summary

Evidence Ledger

Release & Crawl Timeline

Artifacts Archive

Docs & README

Agentic AI Workshop: Multi-Agent Systems From Idea to Deployment

Workshop Goals

Project Structure

Built-in Agent Tooling

Prerequisites

Installation

Running the Backend Pipeline

Using run_pipeline Programmatically

Running the Streamlit Frontend

Customising Agents and Tasks

Deploying the System

Troubleshooting Tips

Next Steps for Students

Contract & API

Reliability & Benchmarks

Media & Demo

Related Agents

AionUi

activepieces

cherry-studio

CopilotKit

Using `run_pipeline` Programmatically