Juror Cloud

Juror is a GitHub Action that asks several frontier models to review each pull request in parallel. It combines duplicate findings into one report and posts the review, merge score, and cost receipt back to GitHub.

Juror Cloud

This repository also contains cloud/, a deployable hosted companion with GitHub and Google sign-in, GitHub App automation, a lean findings and run inbox, isolated Cloudflare Sandbox execution, post-merge staging QA, and transparent Stripe metered billing. The open-source Action and CLI remain independently usable, while repositories connected to Cloud use hosted execution exclusively.

See the Juror Cloud deployment guide for the GitHub App permissions, Cloudflare resources, secrets, billing meter, and verification commands.

Related MCP server: sentinel-mcp

ChatGPT, Codex, and MCP

Juror Cloud is also available as a remote, OAuth-protected Streamable HTTP MCP server at https://app.juror.dev/mcp. Add that URL in an MCP-compatible client, sign in with your Juror Cloud account, list your workspaces first, and then inspect concise review summaries.

The integration is intentionally narrower than the GitHub Action and local CLI:

• Juror Cloud MCP / Plugin — inspects workspace-scoped findings and hosted runs. It can prepare and, after explicit user confirmation, start or rerun an existing hosted Juror Cloud review. It never accesses arbitrary local repositories or returns raw diffs, full reports, artifacts, screenshots, credentials, or prompt text.

• Juror Cloud — the hosted service that owns workspace membership, GitHub App access, isolated execution, retention, and billing.

• GitHub Action and CLI — run from your repository or local environment using the configuration you control. Do not run the hosted service alongside a Juror GitHub Action for the same repository.

Starter prompts:

• “Show my Juror workspaces and review findings.”

• “Summarize open P0 and P1 Juror findings in this workspace.”

• “Prepare a hosted review for this open PR; show me the SHA and cost warning before starting.”

The canonical registry metadata is registry/server.json. See the directory submission record for publication status and the required external verification steps.

Add Juror to your repository

Start from the Juror AI listing in GitHub Marketplace: click Use latest version to add it to a workflow. If you prefer to add it directly, create .github/workflows/juror.yml with this ready-to-run workflow:

name: Juror

on:
  pull_request:
    types: [opened, synchronize, reopened]

permissions:
  contents: read
  pull-requests: write

concurrency:
  group: juror-${{ github.event.pull_request.number }}
  cancel-in-progress: true

jobs:
  review:
    # Provider secrets are never exposed to code from forks.
    if: github.event.pull_request.head.repo.full_name == github.repository
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 # v4.4.0
        with:
          fetch-depth: 0
          filter: blob:none
      - uses: juror-ai/juror@178a16d6fa3a1b868094bc4236d42fb8e442ea6a # v1.3.3
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
        env:
          JUROR_OPENAI_API_KEY: ${{ secrets.JUROR_OPENAI_API_KEY }}
          JUROR_FIREWORKS_API_KEY: ${{ secrets.JUROR_FIREWORKS_API_KEY }}

Then add at least one provider key in Settings → Secrets and variables → Actions. One key starts a review; adding a second provider makes the default fast preset a cross-model jury:

gh secret set JUROR_OPENAI_API_KEY
gh secret set JUROR_FIREWORKS_API_KEY

Commit the workflow and open a pull request. Juror immediately posts a sticky Juror is reviewing… comment, then replaces it with the findings, merge score, and bill. Every Action input besides github-token is optional; see action.yml for preset, models, config, cost-target-usd, post, and pr-number.

The example pins both Actions to immutable commits. The Marketplace's generated snippet is a convenient starting point; use an immutable SHA in the committed workflow to keep the reviewed Action version fixed.

The CLI can generate the same SHA-pinned workflow, inspect the plan first, and optionally upload provider keys through the GitHub CLI:

export JUROR_OPENAI_API_KEY=…
npx juror-ai init --set-secrets

# Or inspect the planned workflow and secrets without changing anything.
npx juror-ai init --dry-run

To review an existing pull request locally without creating a workflow:

npx juror-ai review --pr 1234 --repo owner/name          # prints to your terminal
npx juror-ai review --pr 1234 --repo owner/name --post   # also posts the review

The optional starter preset uses one JUROR_OPENROUTER_API_KEY to reach two model families:

npx juror-ai init --preset starter --set-secrets

Benchmark evidence: Juror Fast found 4× more adjudicated P0–P2 defects than Greptile

On the bundled production-PR seed, Juror Fast found 4/6 (66.7%) P0–P2 defects at 100% precision. Greptile found 1/6 (16.7%) at 50% precision.

Reviewer	P0–P2 recall	Precision
Juror Fast	66.7% (4/6)	100% (4/4)
Greptile	16.7% (1/6)	50% (1/2)

This is a manually adjudicated, one-PR seed—not a statistically sufficient replacement benchmark. Inspect the corpus and methodology, or reproduce it with juror benchmark --file benchmarks/platform-10359.json.

Optional post-merge browser QA

Juror can also watch merged pull requests, plan affected user journeys, and exercise the live staging or branch deployment through Playwright with structured evidence and, for runs without authentication or supplied browser state, videos. Authenticated runs still interact with the product and evaluate plan-bound checkpoints, but each admitted page-dependent browser call returns the same sealed acknowledgement and completed-attempt outcomes stay hidden from the model:

npx juror-ai init --qa --set-secrets --target-url https://staging.example.com

For a product that exposes a fixed-identity synthetic support-session endpoint, Juror can mint a fresh, single-use login URL for every attempt through qa.auth.session_bootstrap. This v1 mode is restricted to the configured canonical staging origin: set qa.target.preview_fallback: false, and do not expect the session to follow a branch deployment with a different origin. Optional qa.auth.browser_secret_headers are injected only into requests to their exact configured origins, which supports a staging Cloudflare Access service token and a separate WAF header without exposing either credential to other hosts. All logical secrets remain inside JUROR_QA_SECRETS_B64; authenticated visual and trace evidence stays off and browser observations stay sealed.

Use the fixed synthetic-session service, not an arbitrary-user testing-login bypass. The post-merge QA quickstart includes a generic staging example and the required gateway prerequisite.

Repositories can also configure trusted qa.testability.early_exit_paths globs for paths that never justify browser QA. Juror returns a neutral, not-scored no_testable_surface result before deployment lookup, secret loading, model startup, or Playwright only when every path in the complete changed-file manifest matches. The default is empty because an infrastructure or docs tree can still affect a product in some repositories.

The target's exact origin is added to the browser allowlist automatically; repeat --allow-origin for any additional API origin the tested product needs. Without either target option, init writes a safely disabled QA policy and explains how to enable it. The released action verifies its container provenance before credential handoff. See the post-merge QA quickstart for the fastest local loop, staging target setup, synthetic login credentials, and result meanings.

By default, a policy without a trusted reset hook runs navigation-only journeys. A change whose affected journey requires interaction is reported as blocked, not as having no browser surface. Reviewed policy can set qa.sandbox.interaction_policy: read_only to enable click, fill, press, select, and check while Juror blocks non-safe HTTP methods and outbound WebSocket messages after the first action. Persistent mutations still require a dedicated synthetic tenant and qa.sandbox.reset. Each checkpoint fixes its assertion kind and exact locator or URL matcher before the browser opens; authenticated or supplied-state scenarios always run a second sealed attempt, and the controller derives the result from its private ledger. The final report necessarily reveals one bounded pass/fail result per predeclared checkpoint, so connect QA only to a synthetic account and tenant containing no production data or other sensitive records.

Why

Single-model PR bots have three problems, in order of how much they cost you:

1. Blind spots. Every model misses different bugs.

2. Duplication. Multiple reviewers often describe the same defect in different words.

3. Opacity. You pay per seat or per PR and never see what the inference actually cost.

Juror runs several models, uses code-aware similarity plus a conservative referee to deduplicate reports about the same defect, and defaults to high recall: every unique eligible finding is shown. Teams that prefer fewer, higher-confidence findings can switch review.publish_mode to consensus and use model agreement as a precision filter.

And there is no index and no SaaS. A coding agent doesn't need a prebuilt semantic index: the supported agent harnesses all ship repository read/search tools and will go inspect the callers of the function you changed. You get repo-wide context for the price of a few tool calls, with zero indexing infrastructure, zero staleness, and no code leaving the runner beyond the model API call itself.

Non-goals. Not an autofix bot. Not a linter (yours is better and free). Not a chat interface. It reviews a diff and posts findings.

How it works

Each model gets the diff, its own private scratch directory, and read-only access to a clean detached checkout. Their findings then go through five lossless merge stages — cheapest first, with a model call only for possible semantic duplicates:

1. Anchor (free) — snap every finding to a line the diff actually adds or modifies. Findings landing outside the diff are reported separately, never silently dropped.

2. Block (free) — group by file, then by overlapping line window.

3. Exact collapse (free) — normalized identical reports, or identical structured trigger/mechanism/consequence/fix claims, collapse without inference.

4. Similarity + referee (cheap) — weighted prose/symbol similarity nominates possible duplicates. A small call per block merges them only when the faulty mechanism and fix match and their affected behavior substantially overlaps; extra entry points or effects in one report do not make the same bug new. A malformed partition is retried once, then fails open to separate findings so deduplication can never hide a report.

5. Coverage audit (free) — prove every raw atomic finding belongs to exactly one final published or explicitly suppressed result. Any accounting failure discards the merge decisions and falls back to lossless singletons.

In consensus mode an additional verify stage runs: eligible P0/P1 and eligible single-model findings get an adversarial refutation pass. The verifier is asked to refute, and defaults to refuted when the evidence isn't clear.

What it posts

One sticky summary comment, and inline comments delivered as a single batched review — one notification, not twelve. Roughly:

Juror Review

Adds SSE event: error detection to the reasoning stream so mid-stream provider failures retry instead of ending the turn as a silent success.

Merge Confidence: 4/5

Model votes: GPT-5.6 Terra 4 · Grok 4.5 5 · Kimi K3 4 → median 4, capped at 4.5 by 1 confirmed P2.

	Severity	Location	Finding	Agreement
1	P1	src/stream/parse.ts:212	Error branch leaves the reader unlocked	●●● 3/3
2	P2	src/stream/parse.ts:424	Same swallow pattern not ported to the sibling class	●○○ 1/3

Location	Finding	Raised by	Why suppressed
src/stream/parse.ts:387	chunks_emitted hardcoded on error events	GPT-5.6 Terra, Kimi K3	below severity floor

Model	Harness	Input	Cached	Output	Cost	Source
GPT-5.6 Terra	Codex	39.8k	12.1k	8.9k	$0.34	estimated
Grok 4.5	Grok Build	40.1k	0	5.2k	$0.38	reported
Kimi K3	Kimi Code	42.0k	10.0k	4.2k	$0.19	estimated
referee (1 call)	opencode	—	—	—	$0.0011	reported
Total		122k	22.1k	18.3k	$0.91

Plus a file-by-file overview and an optional sequence diagram of the changed flow.

With --post, Juror immediately creates one sticky Juror is reviewing… comment with an animated working indicator and a short progress checklist. The finished summary replaces that same comment in place; failed runs replace it with a terminal error state instead of leaving a spinner behind forever.

Install

GitHub Action

Install from the Juror AI GitHub Marketplace listing or copy the workflow above. npx juror-ai init is an optional guided way to generate that workflow and set secrets; it is not required to use the Action. Beyond github-token, every Action input is optional: preset, models, config, cost-target-usd, post (set false for a dry run), and pr-number. They are documented with their defaults in action.yml.

Locally

The same binary, the same code path, no CI-only surprises:

npm i -g juror-ai

juror review --base main                         # review your working branch
juror review --pr 1234 --repo owner/name         # review a PR, print to the terminal
juror review --pr 1234 --repo owner/name --post  # ...and post it

Put your keys in a .env beside the repo (it is loaded automatically and never committed). Juror copies only committed/staged/tracked working changes into a detached model checkout, so this untracked file is not inside any reviewer read root:

JUROR_ANTHROPIC_API_KEY=…
JUROR_OPENAI_API_KEY=…
JUROR_FIREWORKS_API_KEY=…
JUROR_XAI_API_KEY=…
JUROR_OPENROUTER_API_KEY=…
JUROR_SCALEWAY_API_KEY=…

Supported models & harnesses

Juror normally drives each model through its native agent harness, so each one greps your repo the way its vendor intended. The opt-in one-secret starter uses Juror's confined in-process tool loop against OpenRouter instead; it preserves the same sealed checkout and exact findings file boundary without installing another executable.

DeepSeek models use the DeepSeek-native CodeWhale harness so interleaved reasoning survives tool calls. To add DeepSeek V4.1 Flash on Fireworks to your jury:

models:
  - id: deepseek-v4.1-flash
    harness: deepseek
    harness_model: accounts/fireworks/models/deepseek-v4p1-flash
    pricing_key: accounts/fireworks/models/deepseek-v4p1-flash
    secret: JUROR_FIREWORKS_API_KEY
    args: { reasoning_effort: high }

Scaleway's European OpenAI-compatible endpoint is also built in as scaleway-deepseek-v4-flash-0731. It uses JUROR_SCALEWAY_API_KEY, the deepseek-v4-flash-0731 model id, and reasoning_effort: high; select ultra or add this route to an explicit jury:

models:
  - id: scaleway-deepseek-v4-flash-0731
    harness: generic-openai
    secret: JUROR_SCALEWAY_API_KEY
    base_url: https://api.scaleway.ai/v1
    harness_model: deepseek-v4-flash-0731
    pricing_key: scaleway/deepseek-v4-flash-0731
    args: { reasoning_effort: high }

For a project-scoped or dedicated Scaleway deployment, replace base_url with its public endpoint URL; do not put credentials in that URL.

Configuration

Juror ships five jury presets. Models whose provider key is unavailable are skipped, so ultra means every built-in model that can actually authenticate on that runner.