Orchestration Patterns
Advanced multi-agent orchestration patterns for complex workflows.
Pattern 1: Research → Implement → Review
Use pipeline orchestration to leverage each provider's strengths:
{
"mode": "pipeline",
"steps": [
{
"name": "research",
"agent": "claude",
"promptFile": "research.md",
"model": "claude-sonnet-4-20250514"
},
{
"name": "implement",
"agent": "codex",
"promptFile": "implement.md",
"model": "gpt-5.5"
},
{
"name": "review",
"agent": "cursor",
"promptFile": "review.md"
}
]
}
Why it works: Claude excels at analysis and reasoning, Codex at code generation, and Cursor at IDE-integrated review.
Pattern 2: Provider Race
Compare providers on the same task to find the best fit:
prompts-gpt orchestrate --mode parallel \
--prompt complex-refactor.md \
--providers codex,claude,cursor
When to use: Evaluating which provider handles a specific task type best. Check artifacts to compare output quality, speed, and token usage.
Pattern 3: Quality Gate Pipeline
Pipeline with eval scoring at the end:
# Step 1: Implement
prompts-gpt run implement.md --provider codex
# Step 2: Evaluate
prompts-gpt orchestrate --mode eval \
--prompt eval-implementation.md \
--provider claude \
--criteria correctness,completeness,test-coverage
Pattern 4: Sweep + Cross-Provider Verification
Deep audit followed by independent verification:
# Step 1: Deep sweep with Codex
prompts-gpt sweep security-audit.md -n 5 --provider codex
# Step 2: Verify findings with Claude
prompts-gpt run verify-findings.md --provider claude
Pattern 5: Iterative Refinement with Eval Gating
Sweep with self-evaluation that stops when quality threshold is met:
---
title: Code Quality Improvement
sweep:
defaultIterations: 10
eval:
criteria: [correctness, code-quality, test-coverage]
passThreshold: 0.9
---
The sweep continues until:
- All iterations complete, OR
- Eval score exceeds 0.9 (early termination)
Pattern 6: Multi-Stage Security Audit
# Stage 1: Quick scan (run)
prompts-gpt run quick-security-scan.md --provider codex
# Stage 2: Deep audit (sweep)
prompts-gpt sweep full-security-audit.md -n 5 --provider codex
# Stage 3: Cross-provider verification (parallel)
prompts-gpt orchestrate --mode parallel \
--prompt verify-security.md \
--providers codex,claude
# Stage 4: Final assessment (eval)
prompts-gpt orchestrate --mode eval \
--prompt final-assessment.md \
--provider claude \
--criteria correctness,completeness,thoroughness
Pattern 7: CI Pipeline Integration
# .github/workflows/ai-review.yml
jobs:
quick-check:
runs-on: ubuntu-latest
steps:
- run: prompts-gpt run lint-check.md --provider codex --json
deep-review:
needs: quick-check
runs-on: ubuntu-latest
steps:
- run: prompts-gpt sweep security-audit.md -n 3 --provider codex --json
quality-gate:
needs: deep-review
runs-on: ubuntu-latest
steps:
- run: |
prompts-gpt orchestrate --mode eval \
--prompt quality-check.md --provider claude \
--criteria correctness,completeness --json
Pattern 8: Programmatic Custom Orchestration
Build custom workflows using the SDK:
import { runPrompt, sweepPrompt, orchestrateEval } from "prompts-gpt";
async function customWorkflow() {
// 1. Quick lint
const lint = await runPrompt({
promptFile: "lint.md",
provider: "codex",
});
if (!lint.ok) {
console.error("Lint failed, aborting");
return;
}
// 2. Deep security sweep
const sweep = await sweepPrompt({
promptFile: "security.md",
iterations: 5,
provider: "codex",
eval: { criteria: ["correctness"], passThreshold: 0.8 },
});
// 3. Quality gate
const eval = await orchestrateEval({
promptFile: "final-check.md",
provider: "claude",
criteria: [
{ name: "correctness", weight: 0.4 },
{ name: "completeness", weight: 0.3 },
{ name: "security", weight: 0.3 },
],
});
console.log(`Final score: ${eval.overallScore}`);
return eval.overallScore >= 0.8;
}