LangGraph architecture that scales (hexagonal pattern, 110 tests)
I kept hitting the same wall with LangGraph: tutorials show you how to build a graph, not how to maintain one when you have 8 nodes, 3 agents, and shared state across subgraphs. So I built a reference architecture with: - Platform layer separation (framework-independent core) - Contract validation on every state mutation - 110 tests including architecture boundary enforcement - Patterns that AI coding agents can't accidentally break Repo: https://github.com/cleverhoods/sagecompass Wrote about the patterns:…
LangGraph architecture that scales (hexagonal pattern, 110 tests) launched on January 20, 2026 with 12 votes, #893 of 14,584 launches that month and more than 90% of that year's launches.
In plain words
LangGraph architecture that scales is a reference implementation for building maintainable LangGraph applications at scale. It addresses the gap between basic tutorials and production systems by providing a hexagonal architecture pattern with platform layer separation, contract validation on state mutations, and 110 comprehensive tests including boundary enforcement. Designed for developers building multi-node graphs with multiple agents and shared state, it includes patterns specifically designed to prevent common architectural violations. The project is MIT licensed and available on GitHub.
written from the facts on this page · September 2026
From the sources
Augmented AI decision framework. Contribute to cleverhoods/sagecompass development by creating an account on GitHub.
SageCompass is an augmented decision system that checks whether a business idea actually needs AI before anyone spends time and money building it. # Installing Drupal ddev start ddev drush site:install --existing-config -y ddev drush uli # to get the login url. IMPORTANT: uv run langgraph dev # or with host IF you want to play around the Drupal - LangGraph integration. See Known errors. uv run langgraph dev --host 0.0.0.0 Run Gradio UI cd langgraph uv run pytest -v You Do What? “It's a virtual consultant that applies consistent logic to every AI idea and tells you - with numbers - if it’s worth doing or not.” . ├── docs/ # Documentation assets │ ├── mermaids/ # Mermaid exports/diagrams │…from github.com
In the maker’s words, at launch
I kept hitting the same wall with LangGraph: tutorials show you how to build a graph, not how to maintain one when you have 8 nodes, 3 agents, and shared state across subgraphs. So I built a reference architecture with: - Platform layer separation (framework-independent core) - Contract validation on every state…
Does a similar job
all alternatives →
GraphBit2025 · graphbit.ai · ▲437We built GraphBit to solve the reliability gap in AI frameworks. It’s built on a Rust execution core with a Python layer for accessibility — deterministic orchestration, lock-free concurrency, and built-in observability. We’d love feedback from the HN community, especially from those running multi-LLM or agentic workflows at scale.
I Built LangGraph for SwiftJul 2026 · github.com · ▲25Type-safe tools, on-device inference, multi-agent workflows, memory, and guardrails, in one Swift-native runtime - christopherkarani/Swarm
LangAlpha – what if Claude Code was built for Wall Street?Apr 2026 · github.com · ▲148Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
Zig Topological Sort Library for Parallel Processing2025 · github.com · ▲114I believe the best way to learn a language is by doing an in-depth project. This is my first Zig project intended for learning the ropes on publishing a Zig package. It turns out to be quite solid and performant. It might be a bit over-engineered. This little library is packed with the following features: - Building dependency graph from dependency data. - Performing topological sort on the dependency graph. - Generating dependence-free subsets for parallel processing. - Cycle detection and cycle reporting.
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 21d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, January 2026
the whole month →- IN
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
AI · Jan 2026 · cannoneyed.com
Automatic AI-powered code reviews the moment you open a PR
Dev tools · Jan 2026 · kilo.ai
