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Conductor Examples

This directory contains example workflow files demonstrating various features of Conductor.

Quick Start Examples

simple-qa.yaml

A minimal workflow with a single agent that answers questions. Demonstrates:

  • Basic workflow structure
  • Input parameters
  • Output schema validation
  • Simple routing to $end
conductor run examples/simple-qa.yaml --input question="What is Python?"

Parallel Execution Examples

for-each-simple.yaml

Dynamic parallel processing with for-each groups. Demonstrates:

  • For-each group definition
  • Processing variable-length arrays
  • Loop variable access ({{ item }}, {{ _index }})
  • Aggregating parallel outputs
conductor run examples/for-each-simple.yaml --input items='["apple", "banana", "cherry"]'

parallel-research.yaml

Parallel research from multiple sources. Demonstrates:

  • Static parallel groups
  • Multiple specialized research agents
  • Result aggregation from parallel outputs
conductor run examples/parallel-research.yaml --input topic="Renewable energy"

parallel-validation.yaml

Parallel code validation checks. Demonstrates:

  • Static parallel groups for concurrent validation
  • Multiple validation agents (security, performance, style)
  • Failure mode configuration
conductor run examples/parallel-validation.yaml --input code="def hello(): print('world')"

Human-in-the-Loop Examples

design-review.yaml

An iterative design workflow with human approval. Demonstrates:

  • Multiple agents with conditional routing
  • Loop-back patterns for refinement
  • Human gates for approval decisions
  • Context accumulation between iterations
  • Safety limits (max_iterations, timeout)
# Interactive mode
conductor run examples/design-review.yaml --input requirement="Build a REST API"

# Automation mode (auto-approves)
conductor run examples/design-review.yaml --input requirement="Build a REST API" --skip-gates

Multi-Agent Workflows

research-assistant.yaml

A multi-agent research workflow with tools. Demonstrates:

  • Multiple specialized agents
  • Tool configuration at workflow and agent levels
  • Explicit context mode
  • Conditional routing based on coverage
  • Complex output schemas
conductor run examples/research-assistant.yaml --input topic="AI in healthcare"

# With custom depth
conductor run examples/research-assistant.yaml --input topic="Quantum computing" --input depth="comprehensive"

research-assistant-claude.yaml

The research assistant configured for Claude provider. Demonstrates:

  • Claude provider with multi-agent workflow
  • Model selection per agent
export ANTHROPIC_API_KEY=sk-ant-...
conductor run examples/research-assistant-claude.yaml --input topic="Machine learning"

multi-provider-research.yaml

Research workflow demonstrating multi-provider patterns. Demonstrates:

  • Provider configuration options
  • Cross-provider workflow patterns
conductor run examples/multi-provider-research.yaml --input topic="Cloud computing"

Planning and Implementation

plan.yaml

A comprehensive design and implementation planning workflow. Demonstrates:

  • Architect agent for creating solution designs with implementation plans
  • Reviewer agent for quality assessment of design and actionability
  • Loop-back pattern until quality threshold is met (score >= 85)
  • Structured output following design document and implementation plan templates
  • MCP server integration (web-search, context7) for research
  • Traceability between requirements and implementation tasks
conductor run examples/plan.yaml --input purpose="Build a user authentication system with OAuth2"

# Resume from existing plan
conductor run examples/plan.yaml --input purpose="..." --input existing_plan="path/to/plan.md"

# With verbose output
conductor -V run examples/plan.yaml --input purpose="..."

implement.yaml

An epic-based implementation workflow with multi-tier review. Demonstrates:

  • Coder agent (Opus 4.5) for deep research, analysis, and implementation
  • Epic reviewer agent (Opus 4.5) for per-epic quality assessment
  • Committer agent (Sonnet) for git commits and plan updates
  • Plan reviewer agent (Opus 4.5) for holistic review of all changes
  • Fixer agent (Opus 4.5) for addressing plan-level issues
  • Iterative epic-by-epic implementation with automatic plan tracking
  • Two-tier review: epic-level (fast) and plan-level (thorough)
conductor run examples/implement.yaml --input plan="path/to/implementation.plan.md"

# With specific epic
conductor run examples/implement.yaml --input plan="..." --input epic="EPIC-001"

# With verbose output
conductor -V run examples/implement.yaml --input plan="..."

Running Examples

Prerequisites

  1. Install Conductor:

    uvx conductor

  2. Ensure you have valid credentials for your provider:

    • Copilot: GitHub authentication via gh auth login
    • Claude: Set ANTHROPIC_API_KEY environment variable

Validate Before Running

You can validate a workflow without executing it:

conductor validate examples/simple-qa.yaml

Dry Run

Preview the execution plan without actually running the workflow:

conductor run examples/simple-qa.yaml --dry-run

Verbose Mode

See detailed execution progress:

conductor -V run examples/simple-qa.yaml --input question="Hello"

Web Dashboard

web-dashboard-test.yaml

A multi-pattern workflow for testing the web dashboard. Demonstrates:

  • Real-time DAG visualization with live node state updates
  • Agent detail panel with streaming reasoning and tool calls
  • Sequential, parallel, and script step patterns in a single workflow
# Foreground dashboard (keeps running after workflow completes)
conductor run examples/web-dashboard-test.yaml --web --input topic="Python async programming"

# Background mode (prints URL and exits immediately)
conductor run examples/web-dashboard-test.yaml --web-bg --input topic="Rust vs Go"

Creating Your Own Workflows

Use the init command to create a new workflow from a template:

# List available templates
conductor templates

# Create from a template
conductor init my-workflow --template loop

Tips

  1. Start simple: Begin with a linear workflow and add complexity incrementally.

  2. Validate often: Use conductor validate to catch configuration errors early.

  3. Use dry-run: Preview execution with --dry-run before running expensive workflows.

  4. Explicit context: Use context.mode: explicit for complex workflows to control exactly what context each agent sees.

  5. Safety limits: Always set appropriate max_iterations and timeout_seconds for workflows with loops.

  6. Optional dependencies: Use ? suffix for optional input references to avoid errors when agents haven't run yet.

See Also