🤖 PackIA: Machine Learning and AI
From fine-tuning to agents with the best frameworks
Direct answer: From fine-tuning to agents with the best frameworks This pack brings together 8 resources for Claude, Claude Code, ChatGPT Codex.
Build with LangGraph, CrewAI, and the OpenAI Agents SDK: RAG, LLM evaluation, fine-tuning, and multi-agent orchestration with up-to-date guides and production patterns.
Included in the Completo — $50
Plans are cumulative: the Pro Plan includes all Essential Plan packs, and the Complete Plan includes all packs (Essential + Pro). This pack unlocks with the Completo ($50).
Buy with the Completo ($50) Or build your custom plan
Use cases
- A complete RAG pipeline with answer-quality evaluation
- A multi-agent team with CrewAI for your business case
- A prompt evaluation system with datasets and scorers
Master prompt: Agentic ML Engineer
You are an ML engineer. Problem: {problem} with data {data}. 1) Decide the architecture: pure LLM, RAG, or fine-tuning, with cost/quality criteria, 2) implement the prototype with {framework} including context management and tools, 3) define evaluation: a golden-answer dataset, metrics (faithfulness, relevance), and judges, 4) a production plan: caching, observability, and security guardrails.
Included resources (8)
- LangGraph (Agent, langchain-ai/langgraph, ★ 41.8k) — Low-level orchestration for stateful, long-running agents, with deterministic or dynamic flows. GitHub
- CrewAI (Agent, crewAIInc/crewAI, ★ 58.6k) — Orchestrate AI agents with roles (AI teams) through production-ready high-level abstractions. GitHub
- OpenAI Agents SDK (Agent, openai/openai-agents-python, ★ 29.5k · official) — Official OpenAI SDK for agentic apps: agents with tools, handoffs, guardrails, and MCP support. GitHub
- smolagents (Agent, huggingface/smolagents, ★ 29.3k · official) — Minimalist Hugging Face framework for code agents: agents that write Python as action. GitHub
- Deep Agents (Agent, langchain-ai/deepagents, ★ 29.5k) — LangChain's batteries-included agent: plan, read and write files, and delegate to subagents for long tasks. GitHub
- claude-api (Skill, anthropics/skills, ★ 176.6k · official) — Claude API reference: models, pricing, parameters, streaming, tool use, MCP, and token counting. GitHub
- mcp-builder (Skill, anthropics/skills, ★ 176.6k · official) — Official guide for creating high-quality MCP servers in Python (FastMCP) or TypeScript, with tools and evaluation suites. GitHub
- Prompt Engineering Guide (Prompt collection, dair-ai/Prompt-Engineering-Guide, ★ 78.4k) — Guide, papers, and notebooks on prompt engineering, RAG, and agents: few-shot, CoT, and self-consistency. GitHub
Versions by tool
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Frequently asked questions
What is the Machine Learning and AI pack?
From fine-tuning to agents with the best frameworks Includes 8 resources.
Who is it for?
For professionals and teams working in Machine Learning and AI who want proven resources for their AI assistant.
What does it include and how much does it cost?
Includes 8 resources (Agent (5), Prompt collection (1), Skill (2)) and unlocks with the Complete Plan ($50). Plans are cumulative.