COMandA

Declarative AI pipelines for the command line. Define LLM workflows in YAML, run them anywhere, version control everything.

# Pipe code through multiple AI agents
cat main.go | comanda process code-review.yaml

# Compare how different models solve a problem
comanda process model-comparison.yaml

# Run Claude Code, Codex, and Gemini CLI in parallel
echo "Design a REST API" | comanda process multi-agent/architecture.yaml

Why comanda?

For AI-powered development workflows:

• Run Claude Code, OpenAI Codex, and Gemini CLI side-by-side
• Chain multiple agents for code review → test generation → documentation
• Get diverse perspectives on architecture decisions

For reproducible AI pipelines:

• YAML workflows you can version control and share
• Same workflow runs locally, in CI, or on a server
• Switch providers without changing your pipeline

For command-line power users:

• Pipes, redirects, scripts—works like grep or jq
• Process files, URLs, databases, screenshots
• Batch operations with wildcards and parallel execution

Quick Start

Install

# macOS
brew install kris-hansen/comanda/comanda

# Or via Go
go install github.com/kris-hansen/comanda@latest

# Or download from GitHub Releases

Configure

comanda configure
# Select providers (OpenAI, Anthropic, Google, Ollama, Claude Code, etc.)
# Enter API keys where needed

Run Your First Workflow

Create hello.yaml:

generate:
  input: NA
  model: gpt-4o
  action: Write a haiku about programming
  output: STDOUT

comanda process hello.yaml

Multi-Agent Workflows

Run multiple agentic coding tools in parallel and synthesize their outputs:

parallel-process:
  claude-analysis:
    input: STDIN
    model: claude-code
    action: "Analyze architecture and trade-offs"
    output: $CLAUDE_RESULT

  gemini-analysis:
    input: STDIN
    model: gemini-cli
    action: "Identify patterns and best practices"
    output: $GEMINI_RESULT

  codex-analysis:
    input: STDIN
    model: openai-codex
    action: "Focus on implementation structure"
    output: $CODEX_RESULT

synthesize:
  input: |
    Claude: $CLAUDE_RESULT
    Gemini: $GEMINI_RESULT
    Codex: $CODEX_RESULT
  model: claude-code
  action: "Combine into unified recommendation"
  output: STDOUT

echo "Design a real-time collaborative editor" | comanda process architecture.yaml

Supported Agents

Agent	Model Names	Best For
Claude Code	claude-code, claude-code-opus, claude-code-sonnet	Deep reasoning, synthesis
Gemini CLI	gemini-cli, gemini-cli-pro, gemini-cli-flash	Broad knowledge, patterns
OpenAI Codex	openai-codex, openai-codex-o3	Implementation, code structure

No API keys needed for these—they use their own CLI authentication.

Common Use Cases

Code Review Pipeline

review:
  input: "src/*.go"
  model: claude-code
  action: "Review for bugs, security issues, and improvements"
  output: review.md

Data Analysis

comanda process analyze.yaml < quarterly_data.csv

Model Comparison

parallel-process:
  gpt4:
    input: NA
    model: gpt-4o
    action: "Write a function to parse JSON"
    output: gpt4-solution.py

  claude:
    input: NA
    model: claude-3-5-sonnet-latest
    action: "Write a function to parse JSON"
    output: claude-solution.py

compare:
  input: [gpt4-solution.py, claude-solution.py]
  model: gpt-4o-mini
  action: "Compare these implementations"
  output: STDOUT

Server Mode

Turn any workflow into an HTTP API:

comanda server
curl -X POST "http://localhost:8080/process?filename=review.yaml" \
  -d '{"input": "code to review"}'

Features

Feature	Description
Multi-provider	OpenAI, Anthropic, Google, X.AI, Ollama, vLLM, Claude Code, Codex, Gemini CLI
Parallel processing	Run independent steps concurrently
Tool execution	Run shell commands (ls, jq, grep, custom CLIs) within workflows
File operations	Read/write files, wildcards, batch processing
Vision support	Analyze images and screenshots
Web scraping	Fetch and process URLs
Database I/O	Read from and write to PostgreSQL
Chunking	Auto-split large files for processing
Memory	Persistent context via COMANDA.md
Branching	Conditional workflows with defer:
Visualization	ASCII workflow charts with comanda chart

Visualize Workflows

See the structure of any workflow at a glance:

comanda chart workflow.yaml

+================================================+
| WORKFLOW: examples/parallel-data-processing... |
+================================================+

+------------------------------------------------+
|            INPUT: examples/test.csv            |
+------------------------------------------------+
                        |
                        v
+================================================+
| PARALLEL: parallel-process (3 steps)           |
+------------------------------------------------+
  +----------------------------------------------+
  | [OK] analyze_csv                             |
  | Model:  gpt-4o-mini                          |
  | Action: Analyze this CSV data and            |
  +----------------------------------------------+
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  +----------------------------------------------+
  | [OK] extract_entities                        |
  | Model:  gpt-4o-mini                          |
  | Action: Extract all named entities           |
  +----------------------------------------------+
+================================================+
                        |
                        v
+------------------------------------------------+
| [OK] consolidate_results                       |
| Model:  gpt-4o                                 |
| Action: Create a comprehensive data analysis   |
+------------------------------------------------+

                        |
                        v
+------------------------------------------------+
|     OUTPUT: comprehensive-report.txt           |
+------------------------------------------------+

+================================================+
| STATISTICS                                     |
|------------------------------------------------|
| Steps: 4 total, 3 parallel                     |
| Valid: 4/4                                     |
+================================================+

Documentation

• Examples — Sample workflows for common tasks
• Multi-Agent Workflows — Claude Code + Codex + Gemini CLI patterns
• Claude Code Examples — Agentic coding workflows
• Tool Use Guide — Execute shell commands in workflows
• Server API — HTTP endpoints reference
• Configuration Guide — Adding models and providers

Installation Options

Homebrew (macOS)

brew install kris-hansen/comanda/comanda

Go Install

go install github.com/kris-hansen/comanda@latest

Pre-built Binaries

Download from GitHub Releases for Windows, macOS, and Linux.

Build from Source

git clone https://github.com/kris-hansen/comanda.git
cd comanda && go build

Configuration

Provider Setup

comanda configure

Interactive prompts for:

• Provider selection (OpenAI, Anthropic, Google, X.AI, Ollama, vLLM)
• API keys
• Model names and capabilities

Agentic CLI Tools

These use their own authentication—no comanda configuration needed:

# Claude Code
claude --version  # Verify installed

# Gemini CLI
gemini --version

# OpenAI Codex
codex --version

Environment File

Configuration stored in .env (current directory) or custom path:

export COMANDA_ENV=/path/to/.env

Encryption

Protect your API keys:

comanda configure --encrypt

Development

make deps      # Install dependencies
make build     # Build binary
make test      # Run tests
make lint      # Run linter
make dev       # Full dev cycle

See CONTRIBUTING.md for contribution guidelines.

License

MIT — see LICENSE

Acknowledgments

• OpenAI, Anthropic, and Google for their APIs
• Ollama and vLLM for local model support
• The Go community