Find the right AI agent framework

**ARIS** is a methodology-first, Markdown-driven skill system for autonomous ML research workflows. It orchestrates **cross-model collaboration** — Claude Code executes research while an external LLM (Codex, Gemini, or other) reviews work as an adversarial critic. The entire system is files + plain Markdown skills (no database, no framework), making it portable across Claude Code, Cursor, Trae, Codex CLI, and other agents.

AutoHarness tackles a critical LLM agent failure mode: **agents making illegal/invalid actions**.

**47 unique URLs extracted** across 6 categories from 6 HN threads (1,100+ combined points, 418 comments). The HN multi-agent community is skeptical of framework proliferation but hungry for:

The AI agent orchestration market has exploded from $5.25B (2024) to $7.84B (2025), projected to reach $52.62B by 2030 (46% CAGR). The landscape is consolidating around 4 tiers: hyperscaler frameworks (Google ADK, Microsoft Agent Framework, OpenAI Agents SDK, AWS Strands/AgentCore), open-source orchestrators (LangGraph, CrewAI, Agno, PydanticAI, Mastra), protocol standards (MCP, A2A, Agent Skills), and specialized/research frameworks. >40% of agentic AI projects risk cancellation by 2027 due to cost/complexity — the gap between experimentation and production is the central market opportunity.

A mature Ruby multi-agent orchestration framework (~49.3K LOC across 259 non-test source files, 4 gems: SwarmSDK, SwarmCLI, SwarmMemory, ClaudeSwarm-legacy) with sophisticated plugin architecture, 6-pass agent initialization, lazy delegation, Fiber-based circular dependency detection, comprehensive hooks system (13 events, 6 result actions), composable swarms, persistent memory with semantic search, context compaction, and state snapshot/restore. The most architecturally complete open-source agent framework analyzed to date.

LLM agents are amnesiac: they repeat the same failures, miss reusable successful strategies, and cannot automatically apply lessons from past executions. Existing approaches are inadequate: