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This tutorial combines parallel execution with multi-model routing to get independent opinions from four different LLM providers, then synthesizes the results. This is the ensemble pattern — useful when you want diverse perspectives, consensus-based decisions, or protection against any single model’s blind spots.

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The workflow

Ensemble workflow: Start → Fan Out → Opus, Gemini, Codex, Mercury → Merge → Synthesize → Exit
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This workflow requires API keys for all four providers (ANTHROPIC_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, INCEPTION_API_KEY). If a provider key is missing, that branch will fail — but the remaining branches still complete.

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How it works

The workflow has three phases:

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1. Fan-out to four providers

The fork node spawns four parallel branches, each assigned to a different provider via the stylesheet:
Each branch receives the same prompt but runs on a completely different model. The branches execute concurrently and have no knowledge of each other’s responses.

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2. Merge results

The merge node collects all four responses. It waits for every branch — even if some fail. A missing API key or provider outage doesn’t cancel the entire workflow.

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3. Synthesize

The synth node receives all four perspectives in its preamble and produces a unified recommendation. It uses reasoning_effort: high because comparing and synthesizing multiple viewpoints is a harder task than generating any single one.

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Combining patterns

This workflow combines two patterns from earlier tutorials:
  • Parallel execution from Parallel Review — fan-out/fan-in with join policies
  • Model routing from Multi-Model Routing — stylesheet selectors assigning different providers to each node
The key difference from the parallel review tutorial is that here each branch uses a different provider, not just a different prompt. This gives you genuinely independent perspectives — each model has different training data, different reasoning patterns, and different blind spots.

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When to use ensembles

The ensemble pattern is most valuable when:
  • Correctness matters more than speed — e.g., security audits, architectural decisions, spec reviews
  • You want to detect model-specific blind spots — if three models agree and one disagrees, the disagreement is worth investigating
  • You need confidence in a judgment call — consensus across models is stronger than any single model’s opinion
The tradeoff is cost and latency — you’re making 4x the LLM calls. Use single-model workflows for routine tasks and ensembles for high-stakes decisions.

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What you’ve learned

  • Ensemble workflows fan out the same task to multiple providers
  • ID selectors (#opus, #gemini) assign each branch to a specific model
  • A synthesis node compares perspectives and produces a unified result
  • Combine parallel execution and model routing for diverse, independent analysis

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Next

Sub-Workflows

Delegate to reusable child workflows with the supervisor pattern.