Github ReposUpdated 3h agoRank 70
AionUi
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!
MCPOPENCLAW
Crawler Summary
Generative-and-Agentic-AI-SOS-26 answer-first brief
A comprehensive exploration of modern AI, ranging from generative models (VAEs and DDPMs from scratch) to agentic systems (RAG, CrewAI, and ReAct architectures). Generative and Agentic AI A comprehensive exploration of modern Artificial Intelligence, spanning from the mathematical foundations of generative models to the orchestration of autonomous LLM agents. This repository contains raw, from-scratch implementations of Variational Autoencoders (VAEs), Denoising Diffusion Probabilistic Models (DDPMs), and various Agentic architectures including ReAct and Retrieval-Augmented G Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Freshness
Last checked 10/9/2026
Best For
Generative-and-Agentic-AI-SOS-26 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
Agent DossierGITHUB REPOSSafety: 66/100
Generative-and-Agentic-AI-SOS-26
A comprehensive exploration of modern AI, ranging from generative models (VAEs and DDPMs from scratch) to agentic systems (RAG, CrewAI, and ReAct architectures). Generative and Agentic AI A comprehensive exploration of modern Artificial Intelligence, spanning from the mathematical foundations of generative models to the orchestration of autonomous LLM agents. This repository contains raw, from-scratch implementations of Variational Autoencoders (VAEs), Denoising Diffusion Probabilistic Models (DDPMs), and various Agentic architectures including ReAct and Retrieval-Augmented G
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
Swapnilpurohit
Artifacts
0
Benchmarks
0
Last release
Unpublished
Executive Summary
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
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 Ledger
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor (1)
Vendor
Swapnilpurohit
profilemedium
Compatibility (1)
Protocol compatibility
OpenClaw
contractmedium
Security (1)
Handshake status
UNKNOWN
trustmedium
Integration (1)
Crawlable docs
6 indexed pages on the official domain
search_documentmedium
Release & Crawl Timeline
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Artifacts Archive
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
3
Snippets
0
Languages
python
Executable Examples
bash
git clone https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git cd YOUR_REPO_NAME
bash
pip install -r requirements.txt
bash
GROQ_API_KEY="your_api_key_here"
Docs & README
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB REPOS
Editorial quality
ready
A comprehensive exploration of modern AI, ranging from generative models (VAEs and DDPMs from scratch) to agentic systems (RAG, CrewAI, and ReAct architectures). Generative and Agentic AI A comprehensive exploration of modern Artificial Intelligence, spanning from the mathematical foundations of generative models to the orchestration of autonomous LLM agents. This repository contains raw, from-scratch implementations of Variational Autoencoders (VAEs), Denoising Diffusion Probabilistic Models (DDPMs), and various Agentic architectures including ReAct and Retrieval-Augmented G
Full README
Generative and Agentic AI
A comprehensive exploration of modern Artificial Intelligence, spanning from the mathematical foundations of generative models to the orchestration of autonomous LLM agents. This repository contains raw, from-scratch implementations of Variational Autoencoders (VAEs), Denoising Diffusion Probabilistic Models (DDPMs), and various Agentic architectures including ReAct and Retrieval-Augmented Generation (RAG).
📑 Table of Contents
🧠 Part 1: Generative AI
The first half of this project focuses on understanding and implementing generative image models from scratch using PyTorch.
Variational Autoencoders (VAEs)
train_vae.py: A complete pipeline to compress and reconstruct MNIST handwritten digits. Implements the Reparameterization Trick and optimizes the Evidence Lower Bound (ELBO) to create a continuous, interpolatable 2D latent space.
Denoising Diffusion Probabilistic Models (DDPMs)
diffusion_forward.py: Visualizes the forward diffusion process, demonstrating how linear and cosine variance schedules inject Gaussian noise into a clean image over $T$ timesteps.unet.py: A from-scratch implementation of a symmetric down-and-up convolutional U-Net architecture with skip connections and sinusoidal timestep embeddings.train_ddpm.py: The training loop that optimizes the U-Net to predict added noise using the simplified DDPM objective function.sample_ddpm.py: The reverse sampling script that starts from pure Gaussian noise and iteratively denoises it to generate novel, unconditional digits.
🤖 Part 2: Agentic AI
The second half transitions from generating pixels to orchestrating intelligence. We explore how Large Language Models (LLMs) can be structured to think, use tools, and collaborate.
Transformers & Self-Attention
self_attention.py: A pure NumPy mathematical implementation ofScaledDotProductAttentionandMultiHeadAttention. This serves as the foundational building block for modern LLMs, mapping theoretical understanding into raw tensors.
Conversational Memory & Tool Calling
chatbot_memory.py: A terminal-based chatbot that maintains conversational context by managing a rolling history ofuserandassistantmessages via the Groq API.autonomous_agent.py: Demonstrates primitive agentic behavior by instructing an LLM with a strictly typed JSON schema. The model autonomously identifies when it needs external data, requests tool execution, and formulates an answer based on the tool's output.
ReAct Architecture
react_agent.py: A robust, raw-Python implementation of the Reasoning and Acting (ReAct) loop. Without relying on heavy frameworks, this script enforces a rigorousThought -> Action -> Action Input -> Observationcycle, allowing the model to dynamically solve math problems, read local files, and check the system time.
RAG & Multi-Agent Systems
rag_pipeline.py: A Retrieval-Augmented Generation (RAG) system usingChromaDBand HuggingFacesentence-transformers. It embeds local text documents into a vector database, performs similarity searches based on user queries, and feeds the relevant context directly to the LLM.multi_agent.py: Orchestrates a sequential collaboration between two autonomous agents using CrewAI. A Senior AI Research Analyst extracts complex technical concepts, and a Technical Content Strategist transforms those notes into engaging, easy-to-understand educational content.
Capstone: Agentic DDPM Controller
capstone_agent.py: The grand integration of the project. This master ReAct Agent acts as a conversational interface capable of autonomously resolving queries by searching the localChromaDBRAG vector store for AI concepts, and utilizing a custom tool to dynamically load the PyTorch DDPM model, generate novel MNIST digits on command, and save the result to disk.
🚀 Installation & Setup
-
Clone the repository:
git clone https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git cd YOUR_REPO_NAME -
Install dependencies: Make sure you have Python 3.11+ installed.
pip install -r requirements.txt(Note: The
requirements.txtshould includetorch,torchvision,numpy,openai,langchain,langchain-groq,crewai,chromadb, andsentence-transformers.) -
Set up Environment Variables: Create a
.envfile in the root directory and add your Groq API key (required for the Agentic AI scripts):GROQ_API_KEY="your_api_key_here" -
Run the scripts!
- For generative models, start with
python train_vae.py. - For agentic systems, try
python react_agent.pyorpython multi_agent.py.
- For generative models, start with
Developed as part of the Summer of Science (SOS) 2026 Exploration.
Contract & API
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
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-swapnilpurohit-generative-and-agentic-ai-sos-26/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/trust"
Reliability & Benchmarks
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
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.
No screenshots, media assets, or demo links are available.
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Machine Appendix
Contract JSON
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"supportsA2a": false,
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"inputSchemaRef": null,
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"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
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"contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/contract",
"trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/trust"
},
"curlExamples": [
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"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/contract\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
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"generatedAt": "2026-10-09T22:03:25.240Z"
}
},
"retryPolicy": {
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500,
1500,
3500
],
"retryableConditions": [
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]
}
}Trust JSON
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"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",
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},
{
"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": "Swapnilpurohit",
"href": "https://github.com/SwapnilPurohit/Generative-and-Agentic-AI-SOS-26",
"sourceUrl": "https://github.com/SwapnilPurohit/Generative-and-Agentic-AI-SOS-26",
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"observedAt": "2026-10-09T17:02:04.072Z",
"isPublic": true
},
{
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"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/contract",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/contract",
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"confidence": "medium",
"observedAt": "2026-10-09T17:02:04.072Z",
"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-swapnilpurohit-generative-and-agentic-ai-sos-26/trust",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-swapnilpurohit-generative-and-agentic-ai-sos-26/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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