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Integration Examples Guide

Status: stale (2026-05). The shell pipelines referenced below (scripts/examples/integration/*.sh) were retired during the orchestrator consolidation and do not exist on disk. Likewise, the Python snippets import codomyrmex.project_orchestration, which has been renamed to codomyrmex.orchestrator (see src/codomyrmex/orchestrator/__init__.py). For current runnable demos, see src/codomyrmex/examples/agent_orchestration_demo.py and src/codomyrmex/examples/ambitious_swarm_demo.py. The rest of this page is preserved for historical reference until the integration-demo set is rebuilt.

Documentation for integration examples demonstrating multi-module workflows and coordination patterns.

Overview

Integration examples demonstrate how multiple Codomyrmex modules work together to create comprehensive workflows. These examples showcase:

  • Cross-module coordination
  • Multi-step workflows
  • Data flow between modules
  • Resource management
  • Error handling across modules

Available Examples

1. Code Quality Pipeline

File: scripts/examples/integration/code-quality-pipeline.sh

Purpose: Complete code quality analysis workflow combining environment validation, static analysis, and data visualization.

Modules Used:

  • environment_setup - Environment validation
  • static_analysis - Code quality analysis
  • data_visualization - Results visualization
  • logging_monitoring - Structured logging

Configuration:

  • Target directory: Current directory or --target=PATH
  • Analysis tools: Pylint, Flake8, Bandit (automatic selection)
  • Output format: JSON, HTML, PNG

Execution:

./scripts/examples/integration/code-quality-pipeline.sh
# Or with custom target
./scripts/examples/integration/code-quality-pipeline.sh --target=./src

Workflow Steps:

  1. Environment validation and setup
  2. Static code analysis
  3. Quality metrics calculation
  4. Visualization generation
  5. Report compilation

Expected Output:

  • Analysis reports in scripts/output/code-quality-pipeline/
  • Quality metrics dashboard
  • Visualization charts
  • Comprehensive quality report

Duration: ~4 minutes

2. AI-Enhanced Analysis

File: scripts/examples/integration/ai-enhanced-analysis.sh

Purpose: Demonstrates AI-powered code analysis combining static analysis with AI insights.

Modules Used:

  • static_analysis - Initial code analysis
  • agents - AI-powered insights
  • data_visualization - Results visualization

Configuration:

  • AI provider: OpenAI (configurable)
  • Analysis depth: Comprehensive
  • Output formats: JSON, HTML

Execution:

./scripts/examples/integration/ai-enhanced-analysis.sh

Workflow Steps:

  1. Static analysis of codebase
  2. AI analysis of findings
  3. Insight generation
  4. Visualization creation
  5. Report generation

Expected Output:

  • AI-enhanced analysis reports
  • Insight visualizations
  • Recommendations and suggestions
  • Quality improvement metrics

Duration: ~5-7 minutes (depends on AI provider response time)

3. Environment Health Monitor

File: scripts/examples/integration/environment-health-monitor.sh

Purpose: Monitors system health and environment status across multiple components.

Modules Used:

  • environment_setup - Environment checks
  • logging_monitoring - Health metrics
  • data_visualization - Health dashboards

Configuration:

  • Check interval: Configurable
  • Metrics tracked: System resources, module status, dependencies

Execution:

./scripts/examples/integration/environment-health-monitor.sh

Expected Output:

  • Health status reports
  • System metrics dashboards
  • Environment validation results
  • Resource utilization charts

Duration: ~2 minutes

4. Development Workflow Orchestrator

File: scripts/examples/integration/development-workflow-orchestrator.sh

Purpose: Complete development workflow from code analysis to visualization.

Modules Used:

  • Multiple modules coordinated through orchestration

Configuration:

  • Workflow definition: Configurable
  • Resource allocation: Automatic

Execution:

./scripts/examples/integration/development-workflow-orchestrator.sh

Expected Output:

  • Complete workflow results
  • Execution metrics
  • Generated artifacts
  • Performance reports

Duration: ~5 minutes

5. Comprehensive Analysis Pipeline

File: scripts/examples/integration/comprehensive_analysis_pipeline.sh

Purpose: Comprehensive analysis combining multiple analysis types and visualization.

Modules Used:

  • static_analysis - Code analysis
  • pattern_matching - Pattern detection
  • data_visualization - Visualization
  • agents - AI insights

Configuration:

  • Analysis types: Configurable
  • Output formats: Multiple

Execution:

./scripts/examples/integration/comprehensive_analysis_pipeline.sh

Expected Output:

  • Comprehensive analysis reports
  • Pattern detection results
  • Multi-dimensional visualizations
  • AI-generated insights

Duration: ~6-8 minutes

6. AI-Driven Development Workflow

File: scripts/examples/integration/ai_driven_development_workflow.sh

Purpose: AI-assisted development workflow from code generation to analysis.

Modules Used:

  • agents - Code generation
  • static_analysis - Code analysis
  • data_visualization - Results visualization

Configuration:

  • AI provider: Configurable
  • Generation parameters: Customizable

Execution:

./scripts/examples/integration/ai_driven_development_workflow.sh

Expected Output:

  • Generated code samples
  • Analysis of generated code
  • Quality metrics
  • Visualization of results

Duration: ~5-7 minutes

Configuration Requirements

Environment Variables

Some integration examples require environment variables:

# AI provider API keys
export OPENAI_API_KEY="your-key"
export ANTHROPIC_API_KEY="your-key"

# Configuration paths
export CODOMYRMEX_WORKFLOWS_DIR="./workflows"
export CODOMYRMEX_RESOURCE_CONFIG="./resources.json"

Module Dependencies

Integration examples require multiple modules:

# Check module availability
from codomyrmex.project_orchestration import get_orchestration_engine

engine = get_orchestration_engine()
status = engine.get_system_status()
print(status)

Coordination Patterns

Sequential Execution

Modules execute in sequence:

Module A → Module B → Module C

Parallel Execution

Independent modules execute in parallel:

Module A ──┐
           ├─> Module D
Module B ──┘
Module C ──┐
           └─> Module E

Conditional Execution

Modules execute based on conditions:

Module A → [condition] → Module B or Module C

Error Handling

Integration examples handle errors across modules:

  1. Module-Level Errors: Handled within module
  2. Workflow-Level Errors: Propagated and logged
  3. Resource Errors: Automatic cleanup
  4. Timeout Errors: Handled with retries

Best Practices

  1. Configuration: Use configuration files for complex workflows
  2. Resource Management: Specify resource requirements
  3. Error Handling: Implement comprehensive error handling
  4. Logging: Use structured logging for debugging
  5. Monitoring: Monitor execution metrics
  6. Testing: Test workflows with sample data first

Troubleshooting

Module Import Errors

Error: Module not found or import fails

Solution:

# Verify module installation
uv run python -c "import codomyrmex.static_analysis; print('OK')"

# Reinstall if needed
uv sync

Resource Allocation Failures

Error: Resources not available

Solution:

# Check resource configuration
cat resources.json

# Verify resource availability
python -c "from codomyrmex.project_orchestration import get_resource_manager; rm = get_resource_manager(); print(rm.get_resource_usage())"

Workflow Execution Failures

Error: Workflow fails or times out

Solution:

  • Check workflow configuration
  • Verify module dependencies
  • Review execution logs
  • Check resource availability

Related Documentation

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