huangjia2019/agent-design-patterns — A 7×6 framework for agent architecture.

Description and use

**Summary (2‑3 sentences)**
huangjia2019/agent-design-patterns is a 7×6 “grid” framework that codifies 28 reusable agent‑design patterns as runnable Python snippets, each anchored to a specific coordinate on the grid. The repository bundles verified engineering slices from Claude Code, Aider, OpenHands, and DeerFlow, and serves as a practical companion to *Designing AI Agents* (Manning). With 99 GitHub stars and recent updates, it helps turn isolated prompts and tools into repeatable, orchestrated agent workflows.

**Value**

• **Pattern library:** Provides a curated catalogue of proven agent architectures, letting teams skip the trial‑and‑error phase of building multi‑agent systems.
• **Ready‑to‑run code:** Each pattern ships with executable Python examples, enabling rapid prototyping and immediate validation of ideas.
• **Tool‑integration scaffolding:** Includes verified slices for popular LLM‑coding assistants (Claude Code, Aider, OpenHands, DeerFlow), making it easy to add tool‑use pipelines, memory handling, and coordination logic.
• **Educational bridge:** Aligns directly with the concepts in *Designing AI Agents*, so developers can map theory to concrete implementations.

**Practical adoption path**

1. **Proof‑of‑concept:** Clone the repo and run a single pattern (e.g., the “Coordinator” cell) to ensure the environment and dependencies are functional.
2. **Pattern selection:** Identify the grid coordinates that match your use case (e.g., multi‑agent orchestration, tool‑use, persistent memory) and copy the corresponding snippets into your codebase.
3. **Customization:** Replace the placeholder prompts/tools with your domain‑specific logic, leveraging the provided integration points for Claude, Aider, etc.
4. **Iterative testing:** Use the built‑in unit‑style examples to validate each modified pattern before chaining them together.
5. **Scaling:** Once a small workflow is stable, expand to additional grid cells, gradually building a full agent pipeline while keeping the pattern documentation as a design reference.

**Production readiness**

• **Maturity:** Medium. The library is solid for prototypes and internal tooling, with a recent commit (2026‑06‑25) and community interest (≈100 stars).
• **Dependencies:** Primarily Python; however, the primary repo language is listed as HTML, so verify the required packages (e.g., openai, langchain, tool‑specific SDKs) and pin versions.
• **Maintenance:** Activity is modest; a final review of the license, security posture, and maintainer responsiveness is advisable before committing to production.
• **Risk mitigation:** Start with a sandbox deployment, run static analysis and dependency scanning, and establish a process for updating the underlying tool slices (Claude Code, Aider, etc.) as they evolve.

Overall, the project offers a valuable, pattern‑driven foundation for building coordinated AI agents, and with a cautious integration and testing strategy it can move from prototype to production‑grade usage.

Problem

huangjia2019/agent-design-patterns helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

A 7×6 framework for agent architecture. 28 patterns, each placed at a coordinate, runnable Python code with verified engineering slices from Claude Code, Aider, OpenHands, DeerFlow. Companion to Designing AI Agents (Manning) by Jia Huang.

Source