llm-panel

Put the same question to several models independently, then read every answer in full.

Judges run in parallel, never see each other's work, and answer from their own reading of your repo. An optional second round shows each of them the others' findings — anonymised — and asks them to defend or withdraw. The output is a single self-contained HTML page where that second round is grouped by the finding being argued about, so comparing what five models said about one line of code doesn't mean holding five documents in your head.

It is not a voting machine. A panel generates candidate defects; it does not establish truth by counting agreements. Every finding still has to be checked against the code — and the tool's other half, recall/, exists to measure what the panel misses rather than assert what it catches.

llm-panel --diff "Which of these changes is most likely to be wrong?"
panel-report --open          # render the newest run and open it
panel-triage --bad           # which runs went wrong, across every run root

A real run, with the waiting compressed: three judges asked in parallel (two free-tier, one on a ChatGPT plan) landing as they finish, the scoreboard from panel.md, and panel-report rendering it (recording):

The rendered report — the scoreboard counts spend and names who answered; the citation-overlap tables show where the panel's attention landed (three judges reviewing a cline PR, converging on one line of TerminalProcess.ts):

Contents: What's here · Install · Configure your roster · Using it · What it actually catches · On real PRs · Tests · Known limitations

What's here

%%{init: {"theme": "neutral", "flowchart": {"wrappingWidth": 320}}}%%
flowchart TB
  Q["question  (+ --diff, or stdin)"] --> LP["llm-panel"]
  LP --> A["judge A"]
  LP -- "in parallel<br/>blind to each other<br/>reading your repo" --> B["judge B"]
  LP --> C["judge C"]
  A & B & C --> RUN["run directory<br/>one .md and one .prompt.md per judge,<br/>panel.md, run.json"]
  A & B & C -.-> RB
  RB["round 2, only with --rebut<br/>each judge sees the others' findings,<br/>anonymised as Reviewer A / B / C,<br/>and defends or withdraws"] -.-> RUN
  style RB stroke-dasharray: 6 4
  RUN --> REP["panel-report<br/>one self-contained HTML page,<br/>rebuttals grouped by finding"]
  RUN --> TRI["panel-triage<br/>the runs that failed"]

tool	what it does
llm-panel	asks the judges, in parallel, and writes the run to disk
panel-report	renders a run as one self-contained HTML page, grouped by claim
panel-triage	finds the runs that failed, which a listing shows as ordinary rows
recall/panel-recall	measures what the panel misses, against a corpus of planted defects
recall/aacr-upstream	runs the panel over AACR-Bench PRs and hands the findings to upstream's evaluator
recall/aacr-score	invokes that evaluator, and refuses to report a number from a judge that isn't running
claimlib.py	the one measurement boundary: reviews → span-grounded observations
*-controls	the regression suites — 810 controls, every one tied to a defect that shipped

Install

Pure Python 3.11+ standard library. No dependencies, no build step. Each tool is one readable file, so either install route runs identical code:

# as a package (entry points: llm-panel, panel-report, panel-triage)
uv tool install llm-panel        # or: pipx install llm-panel

# or as the files themselves
git clone https://github.com/musharna/llm-panel ~/llm-panel
ln -s ~/llm-panel/{llm-panel,panel-report,panel-triage} ~/.local/bin/

3.11 is a hard floor (the link renderer uses atomic groups, added to re in 3.11); panel-report says so at startup rather than failing part-way through a render.

Judges reach models through command-line tools you install separately — none are bundled, and you need at most one to start:

tool	who it is	billing
codex	OpenAI's CLI	a ChatGPT plan, not metered API
opencode	multi-provider CLI most judges route through	your OpenRouter / HuggingFace keys
claude	Anthropic's CLI	a claude.ai subscription (setting ANTHROPIC_API_KEY switches it to metered)
ollama	local models	free, and no tool loop — see the caveat below

If none are present the panel still runs, fails loudly, exits 4, and tells you what to install. A missing tool is one judge's problem, never the whole panel's.

The read-only agent for opencode judges

opencode judges run as an agent named panelist that can read the repo but not write to it, and llm-panel refuses to start an opencode judge until that agent is defined and verified read-only (exit 9) — opencode's default build agent will happily edit the tree it is reviewing. The definition ships as opencode.jsonc: merge its agent.panelist block into ~/.config/opencode/opencode.jsonc, or keep the file at the root of a repo you review with it — opencode reads project-local config too.

Configure your roster

The built-in judge list is a default, not a fixture — it names the author's accounts. Yours will be different. Point the roster at models you actually have:

Copy roster.example.json to ~/.config/llm-panel/roster.json ($XDG_CONFIG_HOME honoured; $LLM_PANEL_CONFIG wins). It is strict JSON — no comments, no trailing commas — and a malformed config is fatal and names the offending key, because quietly falling back to the built-in roster would run a panel you didn't ask for, and bill you for it:

{
  "default": ["codex", "nemotron", "glm", "kimi", "or-deepseek", "or-grok"],
  "judges": {
    "my-gpt": {
      "transport": "opencode",
      "model": "openrouter/openai/gpt-5.6",
      "family": "OpenAI"
    },
    "big-pickle": null
  }
}

null drops a shipped judge. default is the panel run when --judges is absent. llm-panel --list shows the roster offline and marks config-defined judges. llm-panel --check actually pings each one. llm-panel --help-config prints this schema.

Picking judges

"One per vendor" is not the answer. It is tempting to treat vendor labels as a proxy for independent opinions. The evidence says they aren't: Kohli 2026 measured cross-family judge correlation at φ̄=0.389 against same-family 0.437 — barely different — with the three most correlated pairs being cross-family, and found that restricting to one judge per family made effective independence worse (n_eff 1.93 vs 2.18). Family is display metadata here, not policy.

The six-judge set above did score 6/6 against this corpus where a two-vendor panel scored 4/6, but treat that as debugging evidence, not as a result: the roster was repaired because of what happened on those very fixtures, so the comparison is in-sample, and the six defects live in only two files (effective n≈2, 95% CI 61–100%).

What to actually do: pick judges by what they find on your code, and use panel-recall to measure it. The quantity worth maximising is each judge's marginal rescue rate — how often it catches something every other judge on the roster missed — not how many logos are represented.

Two practical constraints: wall-clock is the slowest judge, not the sum, so one slow model sets the pace for every run; and claude-* are deliberately absent from the default, because when Claude wrote the code under review a Claude judge shares the author's blind spots. Add it explicitly when that isn't the case — it is strong.

Using it

• --diff attaches the working-tree diff, so nobody has to describe the change — including you, who would describe it favourably.
• --rebut adds the anonymised second round. Worth it whenever a finding would trigger real work: the first run of it killed three confident findings that were simply wrong. To be precise about the report's grouping of that round: it keys on the rebuttal letter each finding is given (A1, B2 …), so it collects the discussion around one judge's finding. It is not semantic clustering — two judges independently raising the same underlying defect stay two findings, and without --rebut there is no grouping at all.
• --judges a,b,c overrides the default panel. codex~2 runs the same model a second time as a full, separate judge — its own file, its own letter, its own row. Collapsing repeats would hide exactly the disagreement that makes them worth running.
• --thread NAME keeps a persistent conversation per judge. For design questions, not review.
• Long questions go via stdin: llm-panel - <<'ASK' … ASK.

Judges reading through codex/opencode/claude can read your repo. ollama judges answer from the prompt alone with no tool loop, so they cannot verify a claim against code. Treat their findings accordingly.

The rebuttal round as rendered — every position each judge took on each finding, grouped by the finding under dispute, disagreements marked CONTESTED. This run: four free-tier judges asked to review llm-panel's own failure-classification code; one failed and is reported as harness, the other three upheld 7 findings, rejected 4, and missed 6:

What it actually catches

recall/panel-recall is the part most tools like this don't have: a corpus of defects planted in real code, each one proven to misbehave by execution, so "the panel missed it" is a measurement rather than an impression.

At least one of four independent passes (codex ×2 + claude-opus ×2) matched 25 of 27 known targets in this controlled, single-file Python corpus. That is a keyword-matched lower bound on an easy corpus — not an estimate of real-world code-review capability. 95% CI 76.6–97.9%, and that is before accounting for defects clustering within fixtures.

Read that next to a real-world number. CR-Bench (Nutanix, 2026) builds review tasks from real bugs git blamed out of merged PRs in django, sympy, astropy and scikit-learn, and reports GPT-5.2 + Reflexion at 32.8% recall and 5.1% precision. The gap between that and 25/27 is the corpus, not the panel: hand- planted single-mechanism defects in ~40-line files are far easier than real defects in mature codebases, and the two numbers are not even the same estimand — different agents, different context, different definitions of a hit.

So this corpus is a development instrument, good for controlled A/Bs where ground truth must be known and iteration must be cheap (the abstention experiment below is exactly that). It is not evidence of absolute capability, and no number from it should be quoted as one.

Three results worth knowing before you trust any of the output:

• Recall was limited by the roster, not by the models. The two defects that panel never found — a .get(k, default) that doesn't apply to an explicit null, and a corrupt cache file silently becoming empty — are both found by a six-vendor panel (OpenAI / NVIDIA / Zhipu / Moonshot / DeepSeek / xAI): 4/6 → 6/6 on those two fixtures. The best two judges there, at 4/6 each, beat codex at 2/6 — and both were broken or out of credit until the roster was repaired. If your panel is missing things, check who is actually answering before concluding the models can't see it.

• Running the same model twice recovered nothing. First passes 25/27, with repeats 25/27. The repeat-passes idea is well supported in the literature and did not reproduce here. An earlier grader bug reported +1 and it was an artifact. Adding a different vendor did what adding a second pass of the same one could not.

• Letting judges say "nothing is wrong here" is a precision/recall trade, not a free win either way. One sentence of abstention licence is the whole difference.

  	findings/fixture	false positives
  licence on	0.42	0 / 6 judges
  licence off	2.17	2, from 1 of 5 judges

  Findings-per-fixture is measured on fixtures that do contain defects, where the extra findings were verified true — so the licence suppresses real findings (one judge went 3.00 → 0.00 on files with genuine defects). False positives are measured on p01-exhaustive-codec, the one fixture with proven absence rather than verified scope — which is what makes a false-positive rate computable at all. There, the same judge on the same code abstained with the licence and produced two demonstrably false findings without it (it claimed int and str subclasses were rejected; encode(MyInt(1)) returns 'A').

  So: the licence costs true findings and prevents false ones. Which you want depends on whether chasing a false lead costs you more than missing a real defect. Caveat worth stating: one proven fixture, eleven reviews.

On real PRs (AACR-Bench)

The planted corpus above is a development instrument; the real-world numbers come from running the panel over AACR-Bench PRs and scoring the findings with upstream's own evaluator — a real LLM judge doing path → line → semantic matching, so the numbers are theirs, not a self-graded matcher's. On 18 PRs at full roster (extractor-3 re-measurements, 2026-08-28):

--prompt-style	semantic recall	precision	findings read per validated hit
defect (default)	12.2%	16.5%	6.1
broad	26.0%	13.2%	7.6
volume	25.2%	7.9%	12.6

broad — asking for what a careful maintainer would actually raise — doubles the default's recall (McNemar on paired references, p = 0.0005). But the volume control shows what that class of gain is made of: it is the defect prompt plus one exhaustiveness clause, reaches the same recall (p = 1.0 vs broad), and pays for it with half of broad's precision. On a 35-PR replication the ordering holds on both transports while every arm's precision falls (broad ~9.7%, volume ~5.5–6.1%, ~16–18 findings read per hit). A declared cost cut over all of it settled the product default: it stays defect; the only candidate for a future default change is broad (recall/benchmarks/cost-cut/README.md).

What keeps these numbers honest:

• The variance floor is measured. Re-running the same judge on the same 35 PRs moves up to ±3 human-reference matches of 150, with an evaluator replicate at exactly zero — so effects under ~5–7 pp of recall are re-run noise at this n, which every subgroup claim so far was (recall/benchmarks/results-human-2arm-orgpt/perjudge35/).
• Three earlier readings were withdrawn on re-measurement: a DEFECT/IMPROVEMENT split (my classifier was circular), "broad finds different hits" (pre-registered replication on 35 fresh PRs, p = 0.40), and a transport/harness effect (its 13-PR foothold did not survive a re-run; a same-transport re-run of another judge moved as much). The audit trail is in the benchmark READMEs; nothing above rests on a withdrawn claim.
• Diff-in-prompt review is the measured condition — each panel runs in an empty directory with the diff in the prompt. The paired repo-checkout arm moves recall 12.2% → 15.4% (p = 0.48) while losing 7 of the diff arm's matches and gaining 11: repo access changes what judges attend to more than it strictly adds (recall/benchmarks/results-checkout-3judge/README.md).
• Location agreement overstates semantic agreement ~2x (22.8% of references had a finding at the right file and line; 12.2% had one a judge called the same concern) — which is why scoring is delegated upstream instead of done by a local matcher.
• A degraded roster costs about half the recall (6.5% vs 12.2% with one judge's quota spent and a 300s timeout, same extractor). Check who actually answered before reading any number.
• The panel does not discriminate accepted from rejected reviewer comments (12.2% vs 11.1%, Fisher p = 1.0).
• Unlocated findings are withheld from upstream, not handed over empty — upstream's filters treat a missing path or line as match-everything, and passing them through inflated line matches from 20 to 50 on the first scoring run.

Full run ledgers: recall/benchmarks/results-human-2arm/README.md and results-human-2arm-orgpt/README.md; data licensing in recall/benchmarks/PROVENANCE.md.

Tests

./claimlib-controls              #  83
./llm-panel-controls             # 279
./panel-report-controls          # 310
./panel-triage-controls          #  15
./recall/aacr-upstream-controls  #  96
./recall/aacr-recut-controls     #  27
cd recall && ./panel-recall selftest && python3 validate_corpus.py

CI runs all six suites on every push (Python 3.11 and 3.13).

Every control corresponds to a defect that shipped, and each asserts the fixed behaviour and — where the pre-fix input is representable — that the broken version would have failed on it. An assertion that passes on both the broken and the fixed code tells you nothing.

Known limitations

• The judge roster's shipped defaults will not work for you until you configure it.
• Judges can read the working tree. --diff sends untracked file contents to remote APIs. Don't point it at a repo holding secrets you haven't gitignored.
• A panel is not a jury. Independent models generate candidates; verification against code, tests, and execution is still yours to do.
• Recall is measured on a 27-defect Python corpus. That number does not transfer to other languages or to defect classes the corpus doesn't contain.
• The headline recall numbers are prompt- and condition-specific. They move with --prompt-style, roster health, and diff-vs-checkout context — see On real PRs before quoting any of them.
• Every other fixture has verified scope, not proven absence. Their known unplanted defects are recorded in each truth.json and re-checked by execution in validate_corpus.py, so a judge that finds one is not scored as wrong. Anything not yet recorded still depresses the recall floor by making a true finding look like noise.

License

MIT — see LICENSE. The MIT grant covers the code in this repository; the benchmark data under recall/benchmarks/ contains third-party material (PR diffs and review-comment text) that stays under its upstream terms — see recall/benchmarks/PROVENANCE.md.