Archon vs oh-my-openagent
Two of the top projects in Coding agents and assistants, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Archon
YAML workflow engine that runs AI coding agents through fixed dev steps
oh-my-openagent
Terminal coding agent with multi-model orchestration and git-backed memory
Verdict
Archon ranks higher, 82 to 80. Of the 5 score parts we measure for both, Archon leads on 2, oh-my-openagent on 1 and 2 are even.
- Adoptionoh-my-openagent leads by 21 points, 91 to 70: a wide gap.
- FreshnessEven at 100.
- MaintenanceArchon leads by 10 points, 95 to 85.
- Easy to runArchon leads by 17 points, 67 to 50: a wide gap.
- Agent-readyEven at 85.
Pick Archon if you are
Developers who want repeatable, scripted AI coding runs across repos
- Workflows are YAML committed to the repo, with repo files overriding bundled defaults
- Each run gets its own git worktree, so parallel runs do not conflict
Pick oh-my-openagent if you are
Developers wanting a terminal coding agent that mixes model providers
- Per-model system prompts and subscription login for Claude, ChatGPT, Kimi and GLM
- Memory stored as markdown in a git repo, with a cheap model advising the main agent
| What we compare | Archon | oh-my-openagent |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 70, popular | 91, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 85, healthy |
| Easy to run | 67, easy | 50, easy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 23.7k | 70.1k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | TypeScript | TypeScript |
| Docker | Yes | No |
| GPU | Not needed | Not stated |
| arm64 or Apple Silicon | Mentioned | Mentioned |
Archon
Archon lets you define development processes (plan, implement, validate, review, PR) as YAML workflows in `.archon/workflows/`, mixing bash nodes with AI nodes, loops and approval gates. Each run gets its own git worktree. It runs from a CLI, web UI, Slack, Telegram, Discord or GitHub webhooks, using Claude, Codex or Pi, and ships an `sdlc` workflow pack.
Who it is for: Developers who want repeatable, scripted AI coding runs across repos
Strengths
- Workflows are YAML committed to the repo, with repo files overriding bundled defaults
- Each run gets its own git worktree, so parallel runs do not conflict
- Deterministic bash nodes mix with AI nodes, loops and human approval gates
- Ships sdlc pack: triage, investigate, plan, implement, review, validate, PR
Weaknesses
- Claude provider needs Claude Code installed separately; compiled binaries do not bundle it
- Quick-install binary on x64 requires AVX2 CPUs
- Visual workflow builder is labeled experimental and covers a bounded set of nodes
- Sends anonymous telemetry events by default
- no GPU
- Docker + Compose
- Needs Bun, Claude Code, GitHub CLI, SQLite or PostgreSQL
- Models: Claude, Codex, Pi
oh-my-openagent
OmO installs a native `omo` command that runs a coding agent on senpi, a fork of pi. It supports sign-in to Claude, ChatGPT, Kimi and GLM subscriptions, a keyword (`ulw`, `mass ulw`) that fans a task out into a graph of parallel agents, and a memory layer stored as markdown in a git repository. Config lives in `~/.omo/omo.jsonc` with per-project overrides.
Who it is for: Developers wanting a terminal coding agent that mixes model providers
Strengths
- Per-model system prompts and subscription login for Claude, ChatGPT, Kimi and GLM
- Memory stored as markdown in a git repo, with a cheap model advising the main agent
- Native binary via install script, bun or npm; `omo setup` migrates from the OpenCode edition
- Config, skills and prompts reload live; project .omo/omo.jsonc overrides user config
Weaknesses
- License is SUL-1.0, a non-standard license; check terms before commercial use
- Primary install is a curl-pipe-to-bash script
- README gives no RAM, GPU or system requirements
- Reviews in the README are testimonials, not benchmarks
- Needs Model provider account or API keys
- Models: Claude, ChatGPT, Kimi, GLM
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