argue（argue 命令行）

Argue — Multi-Agent Debate Engine

Structured debates where AI agents analyze independently, cross-examine across rounds, and converge on consensus through voting. Higher-confidence answers than any single model alone.

When to Use

✅ Strategic / architectural decisions with real trade-offs, "Should we X or Y?" with real stakes, risk analysis, confirmation-bias mitigation, pre-commit quality gates on big decisions.

❌ Simple factual lookups, time-critical tasks (debates take 3–7 minutes), open-ended creative generation, questions with obvious answers.

Pre-flight

If argue is not on PATH, install it (confirm with the user first — this is a global install):

npm install -g @onevcat/argue-cli

Then verify and configure:

argue version                          # verify installed (v0.2+)
argue config init --global             # ~/.config/argue/config.json — recommended for agent use

# Add at least 2 agents — `--agent <id>` shorthand creates provider + agent in one shot
argue config add-provider --id codex  --type cli --cli-type codex  --model-id gpt-5.4 --agent codex-agent
argue config add-provider --id gemini --type cli --cli-type gemini --model-id gemini-3.1-pro-preview --agent gemini-agent

Why global by default: a global config is set up once and works from any cwd, and outputs go to ~/.argue/output/<requestId>/ instead of cluttering the current project tree. Use argue config init --local only when a specific project needs its own dedicated agent line-up — that writes ./argue.config.json and outputs to ./out/<requestId>/.

For API providers, SDK adapters, roles, and system prompts, see references/setup.md.

Running Debates

# Basic — 2 agents, 2-3 rounds, auto-consensus
argue run --task "Should we use a monorepo or polyrepo?" --verbose

# With a follow-up action: representative executes once consensus is reached
argue run \
  --task "Review the API design in docs/api.md" \
  --action "Implement the consensus recommendation and open a PR" \
  --verbose

# Open the rendered report in the hosted viewer when the run finishes
argue run --task "..." --view

Useful flags (full list: argue --help):

Debates typically take 3–7 minutes for 2 agents × 3 rounds. Default cap is 20 min per round (and per task, which tracks the round cap by default); bump --per-round-timeout-ms for heavy reviews.

Viewing & Acting on Results

When a run finishes, argue prints the request id and a viewer hint. Open it any time:

argue view                  # most recent run
argue view <request-id>     # specific run

The hosted viewer renders result.json entirely client-side (gzip + base64url in the URL fragment — nothing is uploaded). Use --viewer-url to point at a self-hosted viewer.

To run a follow-up task using a debate result as context:

argue act --result ~/.argue/output/<requestId>/result.json --task "Write a summary blog post"
argue act --result ./out/<requestId>/result.json --task "Implement the changes" --agent codex-agent

Output Files

After every run, argue writes to ~/.argue/output/<requestId>/ (global config) or ./out/<requestId>/ (project-local config):

• result.json — full structured result
• summary.md — markdown report (written on completion)
• events.jsonl — event stream (written live, survives crashes — parse it for partial results if a run is killed)
• error.json — error details (only on failure)

Result status: consensus | partial_consensus | unresolved | interrupted | failed.

If a debate drops below the required participant count, prefer the default interrupted path so downstream tools still get a structured result. Only force onInsufficientParticipants: "fail" when the caller explicitly needs legacy hard-failure semantics.

If you need to parse result.json programmatically, the canonical schema lives at packages/argue/src/contracts/result.ts.

Tips

1. Frame as decisions, not topics. "Should we use SwiftUI or UIKit?" beats "Tell me about SwiftUI".
2. Add context. "Should we use a monorepo? Context: 8 microservices, 3 teams, Node+Go" produces sharper claims.
3. 2–3 agents is the sweet spot. Agents in the same round are dispatched in parallel, so wall-clock is dominated by rounds rather than agent count — adding more agents barely costs time. The real cost is tokens: every extra agent produces its own claims, plus every other agent has to read them as peer context, so token usage grows roughly with N². If the user's config has more than 3 agents, pass --agents a,b,c explicitly to pick a focused subset, or set defaults.defaultAgents in the config file once.
4. Use --action when consensus should drive code changes or another real-world side-effect.

Troubleshooting

For common errors and fixes, see references/troubleshooting.md.