# ed-donner/agents

Repo for the Complete Agentic AI Engineering Course

Repository: https://github.com/ed-donner/agents
Canonical: https://ross.abutalabs.com/products/ed-donner-agents
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-08-24T18:25:45+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 37
- inputs: {"age_days": 530, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6043, forks 5180 (observed 2026-08-28T04:09:34.552381+00:00)

## What it is
A 6-week course repository teaching how to build and deploy autonomous AI agents using OpenAI Agents SDK, CrewAI, LangGraph, Google ADK, Pydantic AI, and MCP. It consists of Jupyter Notebook lessons, setup guides, and supplemental resources.

## Use cases
- learn to build autonomous AI agents
- course on agentic AI engineering
- compare agent frameworks like CrewAI and LangGraph
- hands-on tutorials with OpenAI Agents SDK
- learn MCP and agent deployment
- beginner-friendly AI agent coding course

## When to choose
- you want a structured, guided curriculum for building AI agents
- you prefer learning via Jupyter notebooks with runnable examples
- you want exposure to multiple agent frameworks in one course

## When to avoid
- you need a production-ready agent framework rather than educational material
- you want a zero-cost course without LLM API spend
- you need a maintained software library to depend on

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, llm-inference, mcp, developer-tools
- domain: artificial-intelligence, large-language-models, education, tutorials
- platform: python, cross-platform
- tags: agentic-ai, course, jupyter-notebooks, openai-agents-sdk, crewai, langgraph, pydantic-ai, google-adk, ai-agents

## Member repositories
- ed-donner/agents (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:34.552381+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:48:57.514252+00:00, confidence not recorded.
  - readme: https://github.com/ed-donner/agents (fetched 2026-08-28T04:09:34.552381+00:00, sha a623cd369c47)
- Data as of 2026-08-30T08:39:29.467469+00:00.