# Mathews-Tom/Agentic-Design-Patterns

Agentic Design Patterns

Repository: https://github.com/Mathews-Tom/Agentic-Design-Patterns
Canonical: https://ross.abutalabs.com/products/mathews-tom-agentic-design-patterns
License: MIT
License Family: permissive
Last push: 2025-09-05T17:57:11+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 40, release rhythm 35, longevity 26
- inputs: {"age_days": 364, "days_push": 362, "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 1834, forks 365 (observed 2026-08-28T04:05:42.461067+00:00)

## What it is
A community-compiled repository containing the full text of the book 'Agentic Design Patterns' by Antonio Gulli and Mauro Sauco, organized as markdown chapters. It covers 21 core patterns for building autonomous AI agents, including prompt chaining, routing, planning, memory management, RAG, multi-agent orchestration, and MCP, with working code examples.

## Use cases
- learn design patterns for building AI agents
- understand multi-agent orchestration techniques
- study prompt chaining and routing for LLM applications
- learn how to implement memory management in agents
- get practical examples of RAG and MCP integration
- prepare for building production-grade agentic systems
- find a reference on agent safety and error handling patterns

## When to choose
- you are an engineer, researcher, or product manager moving beyond basic LLM API calls to build robust AI agents
- you want a free, comprehensive written reference with real code examples for agentic patterns
- you need coverage of production concerns like safety guardrails, evaluation, and human-in-the-loop design
- you want a structured curriculum of 21 patterns from foundational to multi-agent architectures

## When to avoid
- you need runnable software or a framework rather than a book to read
- you are looking for a beginner introduction to calling LLM APIs
- you require a formally maintained codebase with releases and issue tracking, since this is compiled book content

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, prompt-engineering, rag, mcp
- domain: large-language-models, tutorials, developer-tools
- platform: -
- tags: book, design-patterns, multi-agent-systems, llm-agents, agentic-ai, educational-resource, ai-agents

## Member repositories
- Mathews-Tom/Agentic-Design-Patterns (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:42.461067+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 2026-08-30T03:18:35.219851+00:00, confidence not recorded.
  - readme: https://github.com/Mathews-Tom/Agentic-Design-Patterns (fetched 2026-08-28T04:05:42.461067+00:00, sha f9f2eaf8e393)
- Data as of 2026-08-30T08:39:29.467469+00:00.