Arena

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A position-driven adversarial arena for AI agents. Host provides context and 2+ opposing positions; arena dispatches local CLI models (Claude, Codex, Gemini, OpenAI, Kimi) to argue each position over multiple rounds and returns the transcript.

A standalone CLI — invoke it from your shell, scripts, or any agent that can run shell commands.

Mental model

• Host doesn't fight. The caller (Claude Code, Codex CLI, scripts) just supplies what should be argued and which positions to argue.
• Position is the unit, not the model. Adversarial value comes from clashing stances, not from "which model wins". Same model with two different system prompts is a valid pair if no other CLI is available.
• Arena owns model dispatch. It picks distinct models when multiple CLIs are healthy, falls back to reusing one when not.

Subcommands

Subcommand	Purpose
arena challenge	Core. Run N positions over R rounds against the supplied context.
arena review	Code-review preset over arena challenge. Spawns attacker positions (default: bug-hunter + security-auditor) on the supplied code/diff.
arena health	List agent CLIs and their availability.
arena mcp	Start arena as a stdio MCP server — exposes each scenario as a tool callable from any MCP client.

Install

# Required: at least one of these CLIs in $PATH
npm install -g @anthropic-ai/claude-cli   # for "claude"
npm install -g @codex-ai/cli              # for "codex" / "openai" / "gemini"
uv tool install kimi-cli                  # for "kimi" (or: pipx install kimi-cli)

Shell (no npm/node required)

Downloads a self-contained native binary from the latest GitHub release. Supports macOS (arm64/x64) and Linux (arm64/x64).

curl -fsSL https://raw.githubusercontent.com/tim101010101/arena/main/install.sh | bash

Installs to ~/.local/bin/arena. Override the directory with ARENA_INSTALL_DIR, or pin a version with ARENA_VERSION:

ARENA_INSTALL_DIR=/usr/local/bin ARENA_VERSION=v0.1.3 \
  curl -fsSL https://raw.githubusercontent.com/tim101010101/arena/main/install.sh | bash

npm

npm install -g arena-mcp        # or: npx arena-mcp

CLI usage

# Adversarial debate — supply your own positions
arena challenge \
  --context "Should we use microservices or a monolith for a 10k-user product with 5 devs?" \
  --position "Pro-microservices: team boundaries justify the split" \
  --position "Pro-monolith: a 5-person team should not carry the ops burden" \
  --rounds 3

# Adversarial code review (positions auto-derived from --focus)
arena review --git-ref feature/auth --focus bugs,security

arena review --files src/login.ts,src/session.ts --focus security

# Override which models to use (must already be healthy)
arena challenge --context "..." --position a --position b --models claude,codex

# Diagnostics
arena health
arena --version
arena --help

MCP server

arena mcp starts a stdio MCP server. Each loaded scenario (challenge, review, and any user-defined ones) is exposed as an MCP tool; a health tool is also included.

Add it to your MCP client config (e.g. Claude Desktop or Claude Code .mcp.json):

{
  "mcpServers": {
    "arena": {
      "command": "arena",
      "args": ["mcp"]
    }
  }
}

Once connected, your AI client can call:

• challenge — supply context (string) and positions (array of ≥2 strings); optional rounds and models.
• review — supply sources (array of source objects: raw, git_ref, git_range, file_list, or patch_file); optional focus, rounds, and models.
• health — returns availability of all local agent CLIs.

Configuration (env vars)

Variable	Default	Notes
ARENA_TIMEOUT_MS	120000	Per-fighter execution timeout
ARENA_DEFAULT_ROUNDS	3	Default rounds when not specified
ARENA_DEFAULT_MODE	parallel	Reserved (challenge runs sequentially)
ARENA_MAX_CONTEXT_SIZE	1000000	Max bytes from sources
ARENA_CLAUDE_MODEL / ARENA_CODEX_MODEL / ARENA_GEMINI_MODEL / ARENA_OPENAI_MODEL / ARENA_KIMI_MODEL	CLI default	Per-adapter model override

Dispatch behavior

positions = ["A", "B"]
available = healthCheckAll().filter(ok)
override  = caller-supplied --models / models[]

pool = override ?? available
fighter[i].model = pool[i % pool.length]

• Prefers distinct models when len(positions) ≤ len(pool).
• Cycles when positions outnumber the pool — same model, different prompts.
• Each fighter gets a unique id (<model>#<i>) so transcripts stay disambiguated.

Development

bun install
bun test          # full suite
bun run build     # produces dist/index.js

License

MIT