📚 Codomyrmex API Reference

"Complete API documentation for all Codomyrmex modules and functionality"

This comprehensive API reference provides detailed documentation for all Codomyrmex modules, including function signatures, parameters, return values, examples, and integration patterns.

🏗️ API Architecture Overview

Codomyrmex provides 4 distinct API layers to accommodate different integration needs:

1. 🐍 Python Module APIs {#module-apis}

Direct imports and function calls from Python code
Type-safe interfaces with comprehensive documentation
Modular design - import only what you need
Extensive examples and usage patterns

2. 💻 CLI APIs

Command-line interface for all functionality
Scripting support for automation workflows
Rich output formatting with JSON/structured data
Comprehensive help system and examples

3. 🤖 MCP Tool APIs

Model Context Protocol tools for AI/LLM integration
Standardized tool calling interface
AI agent compatibility with OpenAI, Anthropic, and other providers
Tool discovery and metadata

4. 🌐 REST APIs (Planned)

HTTP endpoints for web integration
JSON-based communication
Authentication and authorization
Rate limiting and monitoring

📋 API Status Legend

Status	Description	Stability
✅ Stable	Production-ready, fully tested	High - Breaking changes rare
🔄 Evolving	Feature-complete, minor updates	Medium - Minor changes possible
🚧 Developing	Core functionality, active development	Low - API may change
📝 Planned	Not yet implemented	N/A - Subject to change

🏗️ Module API Index

🏗️ Foundation Modules

These modules provide essential infrastructure used by all other Codomyrmex modules.

📋 logging_monitoring

Centralized logging and monitoring system

Function	Description	Status
`setup_logging()`	Initialize structured logging system	✅ Stable
`get_logger(name)`	Get logger instance with proper configuration	✅ Stable
`log_performance(func)`	Decorator for automatic performance logging	✅ Stable
`StructuredLogger`	Advanced logging with JSON formatting	✅ Stable

Quick Example:

from codomyrmex.logging_monitoring import setup_logging, get_logger

# Initialize logging (call once at startup)
setup_logging()

# Get logger for your module
logger = get_logger(__name__)
logger.info("Application started successfully")

🌱 environment_setup

Development environment validation and setup automation

Function	Description	Status
`ensure_dependencies_installed()`	Validate all required dependencies	✅ Stable
`check_and_setup_env_vars(root_path)`	Environment variable validation and setup	✅ Stable
`setup_development_environment()`	Complete development environment setup	✅ Stable
`validate_environment()`	Comprehensive environment health check	✅ Stable

Quick Example:

from codomyrmex.environment_setup import ensure_dependencies_installed, validate_environment

# Ensure all dependencies are available
ensure_dependencies_installed()

# Validate environment configuration
env_status = validate_environment()
print(f"Environment: {'✅ Good' if env_status['valid'] else '❌ Issues found'}")

🔗 model_context_protocol

Standardized AI/LLM communication framework

Component	Description	Status
`MCPToolCall`	Schema for AI tool invocation requests	✅ Stable
`MCPToolResult`	Schema for tool execution responses	✅ Stable
`validate_mcp_tool()`	Tool definition validation	✅ Stable
`MCPServer`	Server implementation for MCP tools	🔄 Evolving

Quick Example:

from codomyrmex.model_context_protocol import MCPToolCall, MCPToolResult

# Create a tool call (as would be sent by an AI)
tool_call = MCPToolCall(
    tool_name="generate_code_snippet",
    parameters={"prompt": "Create a hello world function", "language": "python"}
)

# Execute tool and return result
result = MCPToolResult(
    success=True,
    result={"generated_code": "def hello(): print('Hello, World!')"},
    metadata={"execution_time": 0.5}
)

🤖 AI & Intelligence Modules

🤖 agents

AI-powered code generation, refactoring, and analysis

Function	Description	Status	Example
`generate_code_snippet()`	Generate code from natural language	🔄 Evolving	`generate_code_snippet("Create fibonacci function", "python")`
`refactor_code_snippet()`	Refactor existing code	🔄 Evolving	`refactor_code_snippet(code, "optimize", "python")`
`analyze_code_quality()`	Analyze code quality and suggest improvements	🔄 Evolving	`analyze_code_quality(code, "python")`
`explain_code()`	Generate code explanations	🔄 Evolving	`explain_code(code, "python")`
`generate_documentation()`	Auto-generate code documentation	🔄 Evolving	`generate_documentation(code, "python")`

Quick Example:

from codomyrmex.agents import generate_code_snippet

# Generate a complete function
result = generate_code_snippet(
    prompt="Create a secure user authentication system",
    language="python",
    provider="openai"
)

if result["status"] == "success":
    print("🤖 Generated Code:")
    print(result["generated_code"])
    print(f"⏱️ Generated in {result['execution_time']:.2f}s")

📊 data_visualization

Rich plotting and charting capabilities

Function	Description	Status	Example
`create_line_plot()`	Line charts for trends and time series	✅ Stable	`create_line_plot(x_data, y_data, title="Trends")`
`create_bar_chart()`	Bar charts for comparisons and rankings	✅ Stable	`create_bar_chart(categories, values)`
`create_scatter_plot()`	Scatter plots for correlations	✅ Stable	`create_scatter_plot(x_data, y_data)`
`create_histogram()`	Histograms for distributions	✅ Stable	`create_histogram(data, bins=50)`
`create_heatmap()`	Heatmaps for 2D data patterns	✅ Stable	`create_heatmap(data_matrix, x_labels, y_labels)`
`create_advanced_dashboard()`	Multi-chart interactive dashboards	🔄 Evolving	`create_advanced_dashboard(datasets, layout)`

Quick Example:

from codomyrmex.data_visualization import create_bar_chart
import numpy as np

# Create sample data
languages = ["Python", "JavaScript", "Java", "C++", "Go"]
popularity = [85, 72, 65, 58, 45]

# Create a beautiful bar chart
create_bar_chart(
    categories=languages,
    values=popularity,
    title="Programming Language Popularity (2024)",
    x_label="Programming Language",
    y_label="Popularity Score",
    output_path="language_popularity.png",
    color_palette="viridis"
)

🔍 static_analysis

Multi-language code quality and security analysis

Function	Description	Status	Example
`run_pyrefly_analysis()`	Python-specific static analysis	🔄 Evolving	`run_pyrefly_analysis(["src/"], ".")`
`analyze_code_quality()`	General code quality assessment	🔄 Evolving	`analyze_code_quality(code, "python")`
`check_security_issues()`	Security vulnerability scanning	🔄 Evolving	`check_security_issues(codebase_path)`
`analyze_dependencies()`	Dependency analysis and suggestions	🔄 Evolving	`analyze_dependencies("pyproject.toml")`

Quick Example:

from codomyrmex.coding.static_analysis import run_pyrefly_analysis

# Analyze your Python project
analysis = run_pyrefly_analysis(
    target_paths=["src/codomyrmex/"],
    project_root="."
)

print(f"📊 Analysis Results:")
print(f"📁 Files analyzed: {analysis.get('files_analyzed', 0)}")
print(f"🚨 Issues found: {analysis.get('issue_count', 0)}")
print(f"⚡ Performance score: {analysis.get('performance_score', 'N/A')}")

🏃 code

Secure multi-language code execution

Function	Description	Status	Example
`execute_code()`	Execute code in secure Docker containers	✅ Stable	`execute_code("python", "print('Hello')")`
`validate_language()`	Check if language is supported	✅ Stable	`validate_language("python")`
`list_supported_languages()`	Get all supported languages	✅ Stable	`list_supported_languages()`
`CodeExecutor`	Advanced code execution with resource limits	🔄 Evolving	`CodeExecutor(language="python", timeout=30)`

Quick Example:

from codomyrmex.coding import execute_code

# Execute Python code safely
result = execute_code(
    language="python",
    code="print('Hello from Codomyrmex!')",
    timeout=10  # 10 second timeout
)

print(f"✅ Success: {result['success']}")
print(f"📄 Output: {result['output']}")
print(f"⏱️ Execution time: {result['execution_time']:.3f}s")

🛡️ Secure Cognitive Modules

identity

IdentityManager: Manage 3-tier personas (Blue/Grey/Black)
verify_persona(tier): Bio-cognitive verification
Status: ✅ Stable

wallet

WalletCore: Self-custody key management
recover_access(shards): Natural Ritual recovery
Status: ✅ Stable

defense

DefenseCore: Active defense system
detect_exploit(context): Cognitive exploit detection
Status: ✅ Stable

market

MarketCore: Anonymous marketplace access
post_demand(item): Reverse auction demand posting
Status: ✅ Stable

privacy

PrivacyCore: Digital exhaust management
scrub_crumbs(data): Metadata sanitization
Status: ✅ Stable

Service Modules

deployment

trigger_build(target, config): Build automation
synthesize_code_component(template, params): Code generation
BuildOrchestrator: Main build class
MCP Tools: trigger_build, synthesize_code_component
Status: 🔄 Evolving API

git_operations

repository_manager: Repository management functions
metadata_cli: CLI for metadata operations
repo_cli: Repository CLI operations
Status: 🔄 Evolving API

documentation

generate_documentation_website(): Website generation
build_static_site(): Static site building
DocumentationGenerator: Main documentation class
Status: 🔄 Evolving API

🔌 MCP Tool Reference

Model Context Protocol tools for AI integration:

AI Code Editing Tools

interface GenerateCodeSnippet {
  name: "generate_code_snippet"
  parameters: {
    prompt: string
    language: string
    provider?: "openai" | "anthropic" | "google"
    model_name?: string
  }
  returns: {
    status: "success" | "error"
    generated_code?: string
    explanation?: string
    error_message?: string
  }
}

interface RefactorCodeSnippet {
  name: "refactor_code_snippet"
  parameters: {
    code_snippet: string
    refactoring_instruction: string
    language: string
  }
  returns: {
    status: "success" | "error"
    refactored_code?: string
    explanation?: string
    error_message?: string
  }
}

Code Execution Tools

interface ExecuteCode {
  name: "execute_code"
  parameters: {
    language: string
    code: string
    timeout?: number
    input_data?: string
  }
  returns: {
    status: "success" | "error"
    stdout?: string
    stderr?: string
    exit_code?: number
    execution_time?: number
    error_message?: string
  }
}

Build Synthesis Tools

interface TriggerBuild {
  name: "trigger_build"
  parameters: {
    target: string
    config?: object
  }
  returns: {
    status: "success" | "error"
    build_result?: object
    artifacts?: string[]
    error_message?: string
  }
}

interface SynthesizeCodeComponent {
  name: "synthesize_code_component"
  parameters: {
    template: string
    parameters: object
  }
  returns: {
    status: "success" | "error"
    generated_files?: string[]
    component_info?: object
    error_message?: string
  }
}

🌐 CLI API Reference

Complete command-line interface. See CLI Reference for full details.

Core Commands

codomyrmex check: System health verification
codomyrmex info: Project information display
codomyrmex setup: Environment configuration

AI Commands

codomyrmex generate: AI-powered code generation
codomyrmex refactor: AI-assisted code refactoring

Analysis Commands

codomyrmex analyze: Static code analysis
codomyrmex visualize: Data visualization creation

Execution Commands

codomyrmex execute: Safe code execution
codomyrmex test: Test execution and validation

📊 Usage Patterns

Basic Module Usage

# Foundation setup
from codomyrmex.logging_monitoring import setup_logging, get_logger
from codomyrmex.environment_setup import ensure_dependencies_installed

# Initialize environment
setup_logging()
logger = get_logger(__name__)
ensure_dependencies_installed()

# Use core modules
from codomyrmex.data_visualization import create_line_plot
from codomyrmex.agents import generate_code_snippet

# Create visualization
plot_result = create_line_plot(x_data, y_data, title="My Plot")

# Generate code with AI
code_result = generate_code_snippet("Create a sorting function", "python")

MCP Integration Pattern

from codomyrmex.model_context_protocol import MCPToolCall, MCPToolResult

# Create tool call
tool_call = MCPToolCall(
    tool_name="generate_code_snippet",
    parameters={
        "prompt": "Create a data processing function",
        "language": "python",
        "provider": "openai"
    }
)

# Execute tool (handled by MCP framework)
result = mcp_framework.execute_tool(tool_call)

CLI Integration Pattern

# Scripted workflow
codomyrmex generate "data processor" --language python --output processor.py
codomyrmex analyze processor.py --type security --format json --output analysis.json
codomyrmex execute processor.py --input data.txt --output results.txt

🔍 API Discovery

Programmatic Discovery

from codomyrmex.system_discovery import SystemDiscovery

discovery = SystemDiscovery()
modules = discovery.discover_modules()

for module_name, module_info in modules.items():
    print(f"Module: {module_name}")
    print(f"API Functions: {len(module_info.functions)}")
    print(f"MCP Tools: {len(module_info.mcp_tools)}")
    print(f"Status: {'✅' if module_info.is_importable else '❌'}")

CLI Discovery

# Discover available APIs
codomyrmex discover --detailed --output api-inventory.json

# List available MCP tools
codomyrmex info --modules --format json | jq '.modules[].mcp_tools'

# Show specific module API
codomyrmex docs generate --module agents --format markdown

📚 Documentation Links

Module-Specific API Documentation

Module	API Reference	MCP Tools	Status
agents	API Docs	MCP Tools	🔄 Evolving
data_visualization	API Docs	None	✅ Stable
code	API Docs	MCP Tools	🔄 Evolving
static_analysis	API Docs	MCP Tools	🔄 Evolving
deployment	API Docs	MCP Tools	🔄 Evolving
pattern_matching	API Docs	MCP Tools	🔄 Evolving
git_operations	API Docs	MCP Tools	🔄 Evolving
documentation	API Docs	None	🔄 Evolving
logging_monitoring	API Docs	None	✅ Stable
environment_setup	API Docs	None	✅ Stable
model_context_protocol	API Docs	Schema Only	✅ Stable

Additional References

CLI Reference: Complete command-line interface documentation
Module Overview: Module system architecture
Module Relationships: Inter-module dependencies
Architecture Guide: System design principles

🚀 Getting Started with APIs

New Users

API Developers

Integration Developers

Review MCP Tool Specifications (each module has MCP_TOOL_SPECIFICATION.md in its directory)
Examine CLI Reference
Check Usage Examples (each module has USAGE_EXAMPLES.md in its directory)

Version: 0.1.0 Last Updated: March 2026 API Stability: See individual module documentation for stability guarantees

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Module Index: All Agents
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📚 Codomyrmex API Reference

🏗️ API Architecture Overview

1. 🐍 Python Module APIs {#module-apis}

2. 💻 CLI APIs

3. 🤖 MCP Tool APIs

4. 🌐 REST APIs (Planned)

📋 API Status Legend

🏗️ Module API Index

🏗️ Foundation Modules

🤖 AI & Intelligence Modules

🛡️ Secure Cognitive Modules

Service Modules

🔌 MCP Tool Reference

AI Code Editing Tools

Code Execution Tools

Build Synthesis Tools

🌐 CLI API Reference

Core Commands

AI Commands

Analysis Commands

Execution Commands

📊 Usage Patterns

Basic Module Usage

MCP Integration Pattern

CLI Integration Pattern

🔍 API Discovery

Programmatic Discovery

CLI Discovery

📚 Documentation Links

Module-Specific API Documentation

Additional References

🚀 Getting Started with APIs

New Users

API Developers

Integration Developers

Navigation Links