u/Larry_Potter_

I'm building a micro-SaaS that does automated code reviews. The core is an agent that reads PRs, runs analysis tools, and writes structured feedback. I tried a few frameworks and landed on Karis CLI

The architecture fits well: runtime tools (no LLM) for deterministic analysis (linting, complexity metrics, test coverage), orchestration layer for planning the review, task layer for tracking multi-file PRs

The multi-agent piece is useful for larger PRs: one agent handles security checks, another handles style, another synthesizes the feedback. They share the task context without me building a custom coordination system

Still in early stages, but the layered approach has made it easier to add new analysis tools without breaking the orchestration logic. Anyone else building agent-powered SaaS products? What's your architecture?

I shipped an internal tool using Karis CLI last month and learned a few things worth sharing.

The biggest lesson is the runtime layer (atomic tools, no LLM) is where you spend most of your time. Writing good tools small, focused, returning structured data is the real work. The agent orchestration layer is almost "free" once the tools are solid.

The task management layer was more valuable than I expected. Being able to stop a long-running task and resume it later (with full state) saved me multiple times when API rate limits hit or I needed to context-switch

The multi-agent collaboration piece is still rough around the edges, but the concept is right: having a "scanner" agent and a "writer" agent with shared task context is cleaner than one agent doing everything

Happy to share more details about the tool design patterns that worked. What are others building with agent frameworks?

I've built production agents with LangChain and I've been testing Karis CLI. Here's my honest comparison for "boring but real" automation tasks.

LangChain is flexible, lots of integrations, but the abstraction layers can make debugging painful. When something goes wrong in a chain, it's hard to know which layer failed.

Karis is more opinionated (3-layer architecture), but the layers are explicit. Runtime tools are just code. Orchestration is planning. Task management is state, Failures are easier to diagnose

For exploration and prototyping, LangChain's flexibility is nice. For production automation that needs to be reliable and auditable, Karis CLI's structure is more comfortable.

I'm not saying one is better—they're different tools for different stages. But if you're tired of debugging LangChain chains, Karis CLI's explicit layers might be a relief.