Xpersona

Crawler Summary

End-to-End-Agentic-Ai-Automation-Lab answer-first brief

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML. <div align="center"> 🤖 End-to-End Agentic AI & Automation Lab **A comprehensive, production-grade repository for building, deploying, and managing intelligent AI agents, RAG pipelines, and automated workflows.** $1 $1 $1 $1 $1 $1 • $1 • $1 • $1 • $1 </div> --- 📖 Overview Welcome to the **End-to-End Agentic AI Automation Lab**. This repository is a massive, hands-on engineering playbook demonstrating how to transiti Capability contract not published. No trust telemetry is available yet. 102 GitHub stars reported by the source. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

End-to-End-Agentic-Ai-Automation-Lab 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: 75/100

End-to-End-Agentic-Ai-Automation-Lab

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML. <div align="center"> 🤖 End-to-End Agentic AI & Automation Lab **A comprehensive, production-grade repository for building, deploying, and managing intelligent AI agents, RAG pipelines, and automated workflows.** $1 $1 $1 $1 $1 $1 • $1 • $1 • $1 • $1 </div> --- 📖 Overview Welcome to the **End-to-End Agentic AI Automation Lab**. This repository is a massive, hands-on engineering playbook demonstrating how to transiti

OpenClawself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals102 GitHub stars

Capability contract not published. No trust telemetry is available yet. 102 GitHub stars reported by the source. Last updated 10/9/2026.

102 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Mdalamin5

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Unpublished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. 102 GitHub stars reported by the source. 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

Mdalamin5

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

Protocol compatibility

OpenClaw

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

Adoption signal

102 GitHub stars

profilemedium
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

4

Snippets

0

Languages

python

Executable Examples

bash

git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git
cd End-to-End-Agentic-Ai-Automation-Lab

bash

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

bash

cd 18-e2e-chatbot-mem0-tools-HITL-MCP-RAG
pip install -r requirements.txt

env

OPENAI_API_KEY="your_api_key_here"
ANTHROPIC_API_KEY="your_api_key_here"
TAVILY_API_KEY="your_api_key_here"

Docs & README

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

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML. <div align="center"> 🤖 End-to-End Agentic AI & Automation Lab **A comprehensive, production-grade repository for building, deploying, and managing intelligent AI agents, RAG pipelines, and automated workflows.** $1 $1 $1 $1 $1 $1 • $1 • $1 • $1 • $1 </div> --- 📖 Overview Welcome to the **End-to-End Agentic AI Automation Lab**. This repository is a massive, hands-on engineering playbook demonstrating how to transiti

Full README
<div align="center">

🤖 End-to-End Agentic AI & Automation Lab

A comprehensive, production-grade repository for building, deploying, and managing intelligent AI agents, RAG pipelines, and automated workflows.

GitHub stars GitHub forks Python Version License: MIT Open In Colab

Overview • Key Highlights • Project Architecture • Tech Stack • Getting Started

</div>

📖 Overview

Welcome to the End-to-End Agentic AI Automation Lab. This repository is a massive, hands-on engineering playbook demonstrating how to transition from basic LLM API calls to complex, multi-agent autonomous systems and production-ready AI products.

Whether you are looking to build highly reliable Agentic workflows using LangGraph, orchestrate multi-agent collaboration via AutoGen, implement cutting-edge Model Context Protocol (MCP), or serve fine-tuned local models using vLLM and Unsloth, this repository has you covered.

🚀 Key Highlights

  • Advanced Agentic Frameworks: Deep dives into LangGraph (StateGraphs, subgraphs, memory, HITL) and AutoGen (RoundRobin, Swarm, custom tools).
  • Model Context Protocol (MCP): Industry-grade implementations of Anthropic's MCP for tool execution, web search, and Notion integration.
  • Production RAG Systems: Implementation of Hybrid Search, BM25, LlamaParse, Semantic Routing, and Long/Short-Term Memory (Mem0).
  • AI Workflow Automation: Zero-code/low-code multi-agent orchestration using n8n and LangFlow.
  • LLM Fine-Tuning & Serving: Hands-on pipelines for fine-tuning with LoRA/Unsloth and deploying high-throughput inference endpoints with vLLM.
  • End-to-End Products: Complete full-stack implementations of an AI Interviewer, a Production ATS, and SynapseAI (a stateful, persistent chatbot).

📂 Repository Modules & Projects

The lab is structured progressively. Click to expand each module to see the underlying projects:

<details> <summary><b>1️⃣ Foundations & Data Ingestion (Modules 01 - 02)</b></summary> <br>
  • 01-Pydantic-Data-Validation: Data structuring, field validation, and structured LLM outputs.
  • 02-LangChain-Basics: Embedding models, VectorDBs (FAISS, Pinecone), and basic Retrieval-Augmented Generation (RAG) scratchpads.
</details> <details> <summary><b>2️⃣ LangGraph & Workflow Orchestration (Modules 03 - 04, 13 - 14)</b></summary> <br>
  • 03-LangGraph-Introduction: StateGraphs, Agentic workstations, multi-tool calling.
  • 04-LangGraph-Agentic-Workflows: Agentic RAG, Multi-Agent Supervisors, Human-in-the-Loop (HITL), and Corrective RAG (CRAG).
  • 13-e2e-Deep-Agents: Observation, evaluation, and reliable LangGraph applications.
  • 14-e2e-Ambient-Agent: Building background-running autonomous agents.
</details> <details> <summary><b>3️⃣ AutoGen Multi-Agent Systems (Modules 05 - 09)</b></summary> <br>
  • 05-Autogen-Introduction: Async capabilities, tools, and basic teams.
  • 06-Autogen-HITL-and-Agentic-Orchestrator: Selector Group Chats, Docker code execution, and Graph-based AutoGen.
  • 07-End-To-End-Projects-Autogen: GPT Analyzer (Modular architecture), AI Interviewer.
  • 08-Advanced-Autogen-Team: Swarm logic and Society of Mind teams.
  • 09-Autogen-RAG-and-Memory: Integrating mem0 for cross-session AutoGen memory.
</details> <details> <summary><b>4️⃣ Model Context Protocol (MCP) & n8n (Modules 10 - 12)</b></summary> <br>
  • 10-MCP-All-You-Need: Bridging AutoGen and LangChain with MCP. Lead collector, FireCrawl MCP, and Playwright MCP.
  • 11-MCP-based-End-to-End-Products: Building fast, robust API backends utilizing MCP architectures via ngrok and FastAPI.
  • 12-n8n: High-level automations. Chain of Agents, Social Media Content Generation, parallel agent logic, and Telegram bot integrations.
</details> <details> <summary><b>5️⃣ Production RAG & Guardrails (Modules 17, 19)</b></summary> <br>
  • 17-Guardrails-for-llm: Implementing NeMo Guardrails for secure and constrained LLM outputs.
  • 19-Productions-RAG: Industry-practice RAG including LlamaParse, BM25/Hybrid Search, HyDE, chunking strategies, and Reranking pipelines.
</details> <details> <summary><b>6️⃣ LLM Fine-Tuning & Deployment (Modules 21 - 22)</b></summary> <br>
  • 21-LLM-Deployment-vLLM: Deploying models for high-throughput generation using vLLM and accessing via LangChain SDK.
  • 22-LLM-FineTune-Deployment: Model fine-tuning using Unsloth, LoRA, HuggingFace Pipelines, and quantization setups for edge devices.
</details> <details> <summary><b>7️⃣ End-to-End Full-Stack Projects (Modules 18, 20, 23, 24)</b></summary> <br>
  • 18-e2e-chatbot-mem0-tools-HITL-MCP-RAG: A massive implementation of a fully-featured chatbot with long/short-term memory, PostgreSQL persistence, and streaming UI.
  • 20-e2e-Productions-grade-ATS: End-to-end Applicant Tracking System backed by Alembic, SQLModel, and LangGraph.
  • 23-e2e-multi-agent-plan-research-write-blog: A multi-agent writer architecture with a beautiful web frontend.
  • 24-SynapseAI-parsitence-chatbot: A modern API-first chatbot backend via FastAPI with complex graph routing.
</details>

🛠️ Tech Stack & Tools

Core AI/ML: PyTorch LangChain vLLM HuggingFace

Agentic & Orchestration: LangGraph AutoGen n8n MCP

Backend & Data: FastAPI Postgres Redis Docker

⚙️ Getting Started

1. Clone the Repository

git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git
cd End-to-End-Agentic-Ai-Automation-Lab

2. Set Up Virtual Environment

It is recommended to use conda or venv to manage dependencies.

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install Dependencies

Dependencies may vary per module. Navigate to the specific project folder and install the requirements:

cd 18-e2e-chatbot-mem0-tools-HITL-MCP-RAG
pip install -r requirements.txt

4. Environment Variables

Copy the .env.example file (if available in the module) to .env and add your API keys (OpenAI, Anthropic, HuggingFace, etc.):

OPENAI_API_KEY="your_api_key_here"
ANTHROPIC_API_KEY="your_api_key_here"
TAVILY_API_KEY="your_api_key_here"

🤝 Contributing

This repository is continuously evolving! Contributions, bug reports, and feature requests are highly welcome.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📜 License & Connect

Distributed under the MIT License. See LICENSE for more information.

<div align="center">

Developed with 💡 by Md Al Amin

LinkedIn GitHub

If you find this repository helpful, don't forget to ⭐ star it!

</div>

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-mdalamin5-end-to-end-agentic-ai-automation-lab/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/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.

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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-mdalamin5-end-to-end-agentic-ai-automation-lab/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-10T03:30:21.296Z"
    }
  },
  "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": "Mdalamin5",
    "href": "https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab",
    "sourceUrl": "https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T12:48:04.657Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T12:48:04.657Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "102 GitHub stars",
    "href": "https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab",
    "sourceUrl": "https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T12:48:04.657Z",
    "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-mdalamin5-end-to-end-agentic-ai-automation-lab/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-mdalamin5-end-to-end-agentic-ai-automation-lab/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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