Open Agent Octagon — Productivity skill for Claude Code
Same-task comparison harness for coding agents (Claude Code, Codex, …).
How to install Open Agent Octagon
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open catwithAI/open-agent-octagon and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Open Agent Octagon does
Same-task comparison harness for coding agents (Claude Code, Codex, …). Not a leaderboard — it shows where and why they diverge.
Alternatives in Productivity
- Claude Hud — A Claude Code plugin that shows what's happening - context usage, active tools, running agents, and todo progr 14k ★
- Agentacct — See what your coding agents did and what it cost 665 ★
- Browser Harness JS — Self-healing browser harness that enables LLMs to complete any task 480 ★
README
OpenAgentOctagon
**English** | [中文](#openagentoctagon-中文)
**A same-task, multi-agent comparison harness.** Give the same batch of tasks to several coding / model agents, and compare their execution, reasoning, and final artifacts side by side in one UI.
Octagon (the UFC cage) — agents compete on the same stage, differences laid bare.
One run, seven agents on the same SWE-bench task — scores, cost, and behavior compared column by column.
OpenAgentOctagon is a **comparison tool**, not a leaderboard. Its core output is "the visualized difference between several agents doing the same thing"; scoring is only a quantitative anchor for locating those differences. Each comparison captures three things:
- Execution — tool calls, errors, retries, timing
- Reasoning — task understanding, planning, decision forks, self-correction
- Final artifacts — business state, code files, test results, scores
Stack: Python 3.12+ / FastAPI / SQLite / uv (backend) + React + Vite + TypeScript (frontend).
Framework vs. scenarios
This repo is the **framework**: dispatch, execution, observation, scoring, frontend, and a stable scenario interface (`octagon.env_api`). It ships **no real evaluation scenarios** — only two minimal examples (`envs/example-tool-use`, `envs/example-coding`) for quickstart and contract reference.
Real evaluation **scenarios** (envs) are separate assets, kept in their own repo and mounted via the `envs_path` config. The framework has zero hard dependency on any scenario: at runtime it scans the `envs_path` directory and dynamically loads each scenario's `meta.yaml` / `core.py` / `scorer.py` / `tasks/`.
┌──────────────┐ envs_path points to ┌────────────────────┐
│ OpenAgent │ ──────────────────────► │ your scenario repo │
│ Octagon │
Related Skills
Harness Dispatch
Delegate coding tasks to the agent CLIs you already pay for — a local MCP server routing each task to the best
Agent Eval
Head-to-head comparison of coding agents / models on reproducible tasks, with pass-rate / cost / time / consis
Heddlework
A native, harness-neutral workspace for agent sessions, task graphs, diffs, and durable work — Pi first, Codex
Open Axis
Local control plane that routes engineering tasks across Claude Code, OpenCode, and Codex with real fallback,
Cezar
Cezar is a parallel coding agents orchestrator. Type a task, pick a workflow and an agent — Claude Code, Codex
Forgeo
A scheduled, agent-driven software factory. Keep a plain JSON backlog; Forgeo runs your coding agent (Claude C
Related Agents
Opencode Generalist
OpenCode, the open-source model-agnostic CLI harness, for general coding
Harness Implementer
Implements one coding task from an approved /harness plan. Used by the /harness skill for logic changes, new f
Envoy Runner
Proactively use when Claude Code is stuck, wants a second implementation or diagnosis pass, needs a deeper roo