Nous Research doesn't pretrain — they fine-tune and do RL. Hermes 4 scores 96.3% on MATH-500, NousCoder-14B improves Qwen3-14B's coding ability by 7% using only 24K training samples. But the real moat is Hermes Agent: 236K GitHub stars, #19 globally, 3,000 contributors.
`batch_runner.py` runs thousands of prompts in parallel into ShareGPT-format tool-calling trajectories, lets each prompt name its own container image, and resumes by matching prompt content rather than index. Two quality filters run before you see the data: samples with zero reasoning are discarded, and entries calling hallucinated tool names are dropped at merge time. This is why a research lab builds a personal agent — the agent is the data pipeline.
Hermes install paths come in three support tiers: macOS (Apple Silicon), Windows 10/11, Linux/WSL2, and Docker are Tier 1; Termux and Nix are Tier 2; pip, brew, AUR, and Intel Macs are explicitly unsupported — fixes for those won't be merged. On the upgrade side, `hermes update` snapshots state first, then compiles nine critical files after the pull and hard-resets the checkout if any fail to parse.
Hermes Agent is Nous Research's MIT-licensed agent framework, built around a learning loop: it writes its own skills, curates its memory, and searches past sessions with FTS5. It ships `hermes claw migrate` to move you off OpenClaw — but OpenClaw was not replaced, and both projects are still moving. This is the series opener: what it is, how it differs, and when not to pick it.
Autoreason replaces the traditional critique-and-revise loop with a competitive multi-version evaluation mechanism (A/B/AB + blind Borda count), solving three structural problems in LLM self-refinement: prompt bias, scope creep, and lack of restraint.