Pradeep Patel on GitHub
From Projects to Intelligence | PATEL AI Delivery Framework | Agentic AI in Action. Curating and building the open-source AI ecosystem — one repository at a time.
My Repositories
Hands-on projects, forks, and experiments spanning Agentic AI, RAG, LLMs, and enterprise delivery — each mapped to the PATEL Model™.
12 Lessons to Get Started Building AI Agents
Tutorials and implementations for GenAI Agent techniques, from basic to advanced.
Advanced techniques for Retrieval-Augmented Generation (RAG) systems.
Official code repo for O’Reilly Book — Hands-On Large Language Models.
Complete 0-to-100 roadmap for AI, ML, GenAI, Deep Learning, NLP and more.
Playground for latest Front End designs and technologies for better UX.
100 Recommended Repositories
Handpicked open-source repositories across the full PATEL Model™ spectrum — from precision tooling and AI augmentation through transformational agents, execution infrastructure, and lifecycle governance. Star what you need, fork what you will use.
Precision-Led (21 repos)
Repos that ensure accuracy, quality, observability, and data precision in AI systems.
Framework for evaluating LLMs and LLM systems — precision-first assessment.
Elixir framework for building production LLM apps with strict typing.
LLM evaluation framework — unit tests for your AI outputs.
Open-source LLM engineering platform: traces, evals, prompt management.
AI observability platform — trace and evaluate LLM and ML models.
Data and ML logging for quality monitoring in production pipelines.
Data validation and documentation to ensure precision in data pipelines.
Tensor search for text and images — precision retrieval for AI systems.
Unified API for 100+ LLMs with fallbacks, cost tracking, and rate limits.
Open-source vector database for semantic search with precision retrieval.
High-performance vector similarity search engine built for AI precision.
AI-native open-source embedding database for RAG and semantic search.
Cloud-native vector database for enterprise-scale AI applications.
Quality and safety testing framework for AI/ML models.
OpenTelemetry-based observability for LLMs — standardized AI tracing.
Stanford DSPy — framework for programming with foundation models precisely.
Add guardrails to LLM outputs — validation, filtering, and correction.
NVIDIA toolkit for adding programmable guardrails to LLM conversations.
Open-source MLOps platform for experiment tracking and model registry.
MLOps framework for reproducible, production-ready ML pipelines.
Advanced techniques for Retrieval-Augmented Generation (RAG) systems.
AI-Augmented (23 repos)
Core AI/ML frameworks, LLMs, embeddings, fine-tuning, and multimodal capabilities.
SDK to integrate LLMs into apps with enterprise-grade AI augmentation.
Framework for building LLM-powered applications and agent pipelines.
Data framework for LLM-augmented search and knowledge retrieval.
State-of-the-art ML models for NLP, vision, audio, and multimodal AI.
Multi-agent framework where AI agents converse to solve complex tasks.
Framework for orchestrating role-playing, autonomous AI agent crews.
Official Python library for the OpenAI API — GPT-4o, embeddings, tools.
Official Python SDK for Claude — Anthropic’s frontier AI models.
Run Llama 3, Mistral, Gemma locally — AI augmentation without cloud dependency.
Run LLaMA inference on CPU — efficient local AI augmentation for any team.
Fast fine-tuning of LLMs — 5x faster, 80% less memory, same accuracy.
Easy fine-tuning of 100+ LLMs — LoRA, QLoRA, full training for augmentation.
Robust speech recognition — AI augmentation for voice and audio workflows.
Stable Diffusion web UI — visual AI augmentation for content teams.
Contrastive image-text learning — multimodal AI augmentation backbone.
DeepSeek V3 open-weights frontier model for AI-augmented enterprise use.
Official reference implementation of Mistral AI’s open-weights models.
Google DeepMind’s open Gemma models for enterprise AI augmentation.
Parameter-efficient fine-tuning — adapt LLMs with minimal compute cost.
AI-powered semantic search and workflows — augment any enterprise system.
12 Lessons to Get Started Building AI Agents
Tutorials and implementations for GenAI Agent techniques, from basic to advanced.
Official code repo for O’Reilly Book — Hands-On Large Language Models.
Transformational (21 repos)
Autonomous agents, workflow automation, and projects that fundamentally change how work happens.
Workflow automation with 400+ integrations and AI nodes — self-hostable.
Open-source business automation — Zapier alternative with AI capabilities.
Original autonomous AI agent — the project that sparked the agentic movement.
Multi-agent framework that assigns GPT to different software company roles.
LLM agent that autonomously fixes GitHub issues — transformational dev automation.
AI agents that control web browsers — autonomous web task execution.
AI pair programming in the terminal — transforms how developers write code.
Self-hosted AI coding assistant — transformational developer productivity.
Open-source Devin — AI software engineer agent that writes and runs code.
Open platform for language agents in the wild — data, web, and coding agents.
Autonomous agent for online research — produces detailed, fact-based reports.
Open-source conversational AI model to democratize AI transformation.
Reusable computer vision tools — transform visual inspection and automation.
Call 100+ LLM APIs using OpenAI format — transformative unified gateway.
Production-grade agentic AI framework with Pydantic validation.
Open-source platform for building transformational AI chat experiences.
Autonomous AI agents in the browser — no-code transformational deployment.
Code-first agent framework for complex data analysis and task automation.
AI agent for vision tasks — transforms manual image analysis into automation.
The original AI task manager that pioneered autonomous goal decomposition.
Playground for latest Front End designs and technologies for better UX.
Execution (20 repos)
Production deployment, serving, infra, orchestration, and scaling AI systems reliably.
LLM proxy for load balancing, caching, and production execution of AI APIs.
High-throughput, memory-efficient inference engine for LLMs in production.
Language and compiler for GPU kernels — efficient AI model execution.
Distributed computing framework for scaling AI workloads in production.
ML platform for Kubernetes — enterprise-grade AI execution infrastructure.
Model serving framework — build and deploy AI services for production.
NVIDIA Triton — optimized inference serving for all major AI frameworks.
MLOps platform for packaging and deploying ML models on Kubernetes.
End-to-end ML pipelines framework — reproducible execution from dev to prod.
Data orchestration platform for executing production data and AI pipelines.
Platform for programmatically authoring and executing complex workflows.
Modern workflow orchestration for data and AI — retry, schedule, observe.
AI gateway for routing, caching, and executing LLM requests with reliability.
OpenTelemetry-native LLM observability — monitor AI execution end-to-end.
Open-source framework to evaluate and improve LLM app execution quality.
Build, evaluate, and deploy high-quality LLM apps — end-to-end execution.
Python SDK for AI agent monitoring — track execution, costs, and errors.
Run AI workloads on any cloud — cost-optimized, reliable execution at scale.
Open-source feature store for ML — real-time feature serving for execution.
Open platform for training, serving, and evaluating LLM-based chatbots.
Lifecycle (21 repos)
Memory, RLHF, data labeling, governance, standards, and continuous improvement loops.
Transformer Reinforcement Learning — RLHF for fine-tuning LLMs from feedback.
Distributed RL training for LLMs — lifecycle improvement through human feedback.
Train instruction-following LLMs — continuous lifecycle improvement research.
Vicuna and model evaluation via crowd-sourced feedback — lifecycle learning.
Open-source data labelling platform for LLM lifecycle and fine-tuning.
Multi-type data labeling tool — lifecycle foundation for AI training data.
spaCy ecosystem for NLP lifecycle — training, annotation, and deployment.
Memory layer for AI agents — persistent, evolving memory across conversations.
Stateful AI agents with long-term memory and lifecycle persistence (MemGPT).
Long-term memory service for AI assistants — lifecycle knowledge retention.
Responsible AI dashboard — fairness, interpretability, and lifecycle governance.
IBM toolkit for detecting and mitigating AI bias across the model lifecycle.
Fast, Pythonic Model Context Protocol server — standardize agent integrations.
Official MCP server implementations — lifecycle integration standard for agents.
Google A2A protocol Python SDK — agent-to-agent communication standard.
Open-source product analytics — lifecycle tracking for AI product adoption.
Modern data exploration and BI — lifecycle insights for AI delivery metrics.
Business intelligence tool for teams — AI delivery metrics and KPI dashboards.
Framework for evaluating LLMs across hundreds of tasks — lifecycle benchmarking.
HumanEval benchmark for measuring LLM code generation — lifecycle quality gate.
Complete 0-to-100 roadmap for AI, ML, GenAI, Deep Learning, NLP and more.
Want to discuss AI delivery with Pradeep?
These repositories represent the open-source backbone of modern AI transformation. Explore the PATEL Model™ framework to understand how to evaluate and integrate them into your enterprise delivery practice.
