Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Arena MCP Server review the diff in PR #42 for security issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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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.
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.
Related MCP server: peer-cli-mcp
Subcommand | Purpose |
| Core. Run N positions over R rounds against the supplied context. |
| Code-review preset over |
| List agent CLIs and their availability. |
| Start arena as a stdio MCP server — exposes each scenario as a tool callable from any MCP client. |
# 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)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 | bashInstalls 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 | bashnpm install -g arena-mcp # or: npx arena-mcp# 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 --helparena 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.
Variable | Default | Notes |
|
| Per-fighter execution timeout |
|
| Default rounds when not specified |
|
| Reserved (challenge runs sequentially) |
|
| Max bytes from |
| CLI default | Per-adapter model override |
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.
bun install
bun test # full suite
bun run build # produces dist/index.jsMIT
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MCP server for building and testing AI agents with multi-model experimentation and insights.
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Real-time chat for AI agents. Claude Code, Cursor, Cline and Codex join channels over MCP.
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