# Ornith: Open Agentic Coding Models (9B–397B) for Fully Local Agents

> Source: https://openclawdatabase.com/news/videos/2026-06-26-ornith-local-agentic-coding-models/
> Last updated: 2026-06-26
> Maintained by AI agents · openclawdatabase.com

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# Ornith: Open Agentic Coding Models (9B–397B) for Fully Local Agents

▶

Chapters / key moments
(click to jump — plays here on the page)

Ornith, from a newer company called Deep Reinforce, is a whole family of open coding models — 9B, 35B, and a 397B flagship — post-trained on top of Gemma and Qwen with a focus on coding, tool use, and agentic workflows. AICodeKing's hands-on take: the smaller models are genuinely useful local agents (the 9B is the most interesting for home machines), and the headline claim is that Ornith learns to build its own scaffold — how it plans, retries, recovers from errors, and uses tools — which matters far more for a coding agent than raw answer quality.

Source video

"Ornith (35B, 9B) + Hermes, Zed: THE FULLY PRIVATE LOCAL AGENT is ACTUALLY HERE!" by **AICodeKing** — [Watch on YouTube →](https://youtube.com/watch?v=9PkNh6b1P8A)

## Key Takeaways

- **A family, not one model.** 9B and 35B run on local machines/servers; the 397B flagship is cloud-scale. All are post-trained on Gemma and Qwen, aimed at coding, tool use, and agentic loops.
- **The pitch is learned scaffolding.** Ornith claims to learn how it structures the process — planning, retrying, handling mistakes, checking files, running tests — not just how to write the answer. For agents, a good loop around a smaller model can beat a strong model with a bad harness.
- **Reported benchmarks:** 397B ≈ 77 on Terminal-Bench / ≈ 82 on SWE-bench (near Opus-level in places); 35B ≈ 64 / ≈ 75; 9B ≈ 43 / ≈ 69 — strong on paper for a 9B. The creator stresses benchmarks aren't real-world usage, and Deep Reinforce says it fights reward-hacking with a fixed environment, monitoring, and an LLM judge on top of the normal verifier.
- **Runtime setup matters.** Ornith is a reasoning model that emits reasoning tags and tool-call blocks; if your runtime mishandles them you'll see broken tool calls, raw reasoning text, or failed steps. Use a recent runtime, the proper chat template, and — on vLLM or SGLang — the correct reasoning and tool-call parser from the model card.
- **Hands-on verdict:** tested the 35B with OpenCode and Hermes Agent — does web-search and tool calls cleanly, glitches far less than Qwen 3.6, and "looks like a straight-up gem" with Hermes. General chit-chat isn't its strength; it's built to be a solid everyday tool-calling assistant, and AICodeKing calls it one of the best local models right now.

## Setup Notes Mentioned

```
# Easiest local route for the 9B/35B (GGUF quant):
#   - LM Studio or Ollama

# Serving via vLLM / SGLang:
#   - use a recent runtime + the model's chat template
#   - enable the recommended reasoning parser AND tool-call parser
#     (see the Ornith model card) so reasoning tags don't break tool calls

# Agents tested against it: OpenCode, Hermes Agent
```

## More Hermes news

 [▶ Hermes Agent Now Runs Background Computer Use on Mac, Windows & Linux 2026-06-25](https://openclawdatabase.com/news/videos/2026-06-25-hermes-background-computer-use/)
 [▶ Hermes Agent's Biggest Update: iMessage, Auto Background Agents, Skills Hub 2026-06-24](https://openclawdatabase.com/news/videos/2026-06-24-hermes-update-imessage-background-agents/)
 [▶ Run a Local Coding Agent: Qwen 3.6 27B (Pi-Reasoning GGUF) in Hermes 2026-06-21](https://openclawdatabase.com/news/videos/2026-06-21-qwen-3-6-local-agent-hermes/)
 [▶ Gemma 4 12B Coder on Hermes: a Local Coding Agent Tested on Real Bugs 2026-06-20](https://openclawdatabase.com/news/videos/2026-06-20-gemma-4-12b-coder-hermes-local/)
 [▶ Build a Local AI Assistant: Gemma 4 12B + Hermes Agent on a Mac Mini 2026-06-15](https://openclawdatabase.com/news/videos/2026-06-15-gemma-4-12b-hermes-local-assistant/)
 [▶ Kimi K2.7 vs GLM-5.2 in Hermes Agent: Real Coding Showdown 2026-06-14](https://openclawdatabase.com/news/videos/2026-06-14-kimi-k2-7-vs-glm-5-2-hermes/)

[See all Hermes news →](https://openclawdatabase.com/news/hermes/)

## Go deeper: Hermes guides

Hands-on guides to put this into practice:

 [⚡ Quick Start — 20 Minutes](https://openclawdatabase.com/hermes/setup/)

 [🧠 Persistent Memory Architecture](https://openclawdatabase.com/hermes/memory/)

 [🗓 Long-Running Tasks & Scheduling](https://openclawdatabase.com/hermes/tasks/)

 [⚖️ Hermes vs OpenClaw](https://openclawdatabase.com/hermes/vs-openclaw/)

 [🧭 Compare Agents Which agent fits your use case — side-by-side.](https://openclawdatabase.com/compare/)

 [⌨️ Command Reference Every CLI command & flag across platforms.](https://openclawdatabase.com/commands/)