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Fuse the complementary strengths of multiple models into one stronger result.
PanelWise starts with a simple question: if every model sees something worth keeping, can their complementary judgments produce a result stronger than any one answer? It is an embeddable Python aggregation engine that sends the same task to a configurable panel, preserves each model's distinct contribution, and coordinates those contributions through one of two execution topologies. The task itself is unrestricted; prompts, providers, and environment adapters define the domain.
| Mode | Topology | Best fit |
|---|---|---|
--eval |
independent complete attempts → evaluator → synthesis | Answer-centric tasks, including deep research |
--no-eval |
independent next-action proposals → one coordinated action → shared observable state → repeat | Stateful tasks, including coding |
These are execution modes, not hard-coded task categories. Both accept any task string. Custom ChatClient and Executor implementations can connect the same engine to another provider or environment.
panelwise run "Compare PostgreSQL and MySQL for a large marketplace" --eval
panelwise run "Fix the parser regression and run focused tests" --no-eval --workspace ./project--eval and --no-eval override one YAML value, so applications can switch topology without changing code or duplicating configuration.
PanelWise requires Python 3.10 or newer.
git clone https://github.com/inclusionAI/PanelWise.git
cd PanelWise
python3 -m venv .venv
.venv/bin/python -m pip install .Install a pinned Git revision directly:
python3 -m pip install "panelwise @ git+https://github.com/inclusionAI/PanelWise.git@main"Replace main with a release tag or commit SHA when reproducibility matters. Contributors should use python -m pip install -e '.[dev]'.
Create a documented configuration and validate it without making model calls:
panelwise init
export OPENROUTER_API_KEY="..."
panelwise validate --config panelwise.yamlThe generated YAML contains provider, model-role, concurrency, timeout, workspace, and prompt settings. The same file drives both modes:
version: 1
provider:
name: openrouter
api_key_env: OPENROUTER_API_KEY
models:
panel: [z-ai/glm-5.1, minimax/minimax-m3, qwen/qwen3.7-max]
coordinator: z-ai/glm-5.1
evaluator: z-ai/glm-5.1
execution:
eval: true
concurrency: 3
max_steps: 24
workspace: .See the full panelwise.example.yaml and configuration reference.
import asyncio
from panelwise import PanelWise
async def main():
async with PanelWise.from_yaml("panelwise.yaml", eval=True) as engine:
result = await engine.run(
"Compare the strongest arguments for and against carbon taxes.",
request_id="example-1",
)
if result.ok:
print(result.output)
else:
print(result.status, result.errors)
asyncio.run(main())PanelWiseResult has one stable contract across both modes. It always includes request_id, mode, status, output, usage, and errors. Eval mode additionally returns independent candidates and structured evaluation; no-eval mode returns the shared trajectory and executor artifacts, including a Git patch when available.
Individual panel failures are isolated. Invalid configuration raises a documented PanelWiseError subclass; it never terminates the host process with SystemExit. See the Python API and failure contract.
We evaluated PanelWise on all 100 tasks in DRACO, a deep-research benchmark spanning ten domains. Each task is scored against a weighted rubric covering factual accuracy, breadth, depth, presentation, and citation quality.
The OpenRouter Fusion score of 68.3 and Claude Fable 5 score of 65.3 are both quoted from OpenRouter's official Fusion launch post. PanelWise scores come from our complete 100-task evaluation runs.
| System | Models | DRACO score |
|---|---|---|
| PanelWise frontier panel | Opus 4.8 + GPT-5.5 + Gemini 3.1 Pro | 73.68 |
| OpenRouter Fusion, published comparison | Opus 4.8 + GPT-5.5 + Gemini 3.1 Pro | 68.3 |
| PanelWise budget panel | GLM 5.1 + MiniMax M3 + Qwen 3.7 Max | 66.42 |
| Strongest published single-model baseline | Claude Fable 5 | 65.3 |
The frontier panel scored 5.38 points above the published Fusion result using the same three participant models. The independent budget run scored 1.12 points above the strongest published single-model baseline. OpenRouter reports Fable 5 over 93 completed tasks because content filters blocked seven tasks, so that comparison is slightly uneven.
The benchmark supports a claim about deep-research aggregation on DRACO; it is not a claim that every task or model panel improves.
The built-in async client supports:
- OpenRouter;
- ZenMux;
- any OpenAI-compatible chat-completions base URL.
Provider-specific request fields can be supplied through YAML. A custom provider implements the small ChatClient protocol. A custom shared environment implements Executor, allowing no-eval mode to coordinate a shell, container, browser, database, simulator, or remote worker without changing the aggregation engine.
Trajectory mode's built-in local shell executor rejects obvious destructive commands, enforces time and output bounds, and starts every action in one configured workspace. It is a guardrail, not a sandbox; untrusted tasks belong in a disposable container or VM.
- CLI reference
- Configuration and provider setup
- Python API and result contract
- Examples
- Troubleshooting
- Release process
- Contributing and pull request requirements
- Security policy
- Changelog
python3.12 -m venv .venv
.venv/bin/python -m pip install -e '.[dev]'
.venv/bin/ruff check src tests examples
.venv/bin/python -m unittest discover -s tests -v
.venv/bin/python -m buildThe test suite is offline and requires no model key. Public API, provider, executor, and architectural changes should begin with an issue; focused documentation fixes may go directly to a PR. Every behavior change must include reproducible verification. See CONTRIBUTING.md.
Contributors are displayed in the order defined in CONTRIBUTORS.md.
PanelWise is available under the Apache License 2.0.