catwithAI

Open Agent Octagon — Productivity skill for Claude Code

Productivity community

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.

Run detail — 7 agents on the same task, scores side by side
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:

  1. Execution — tool calls, errors, retries, timing
  2. Reasoning — task understanding, planning, decision forks, self-correction
  3. 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      │