Dartantic CLI

cd samples/dartantic_cli
dart pub get

# Simple question
dartantic -p "What is 2+2?"

# Use a specific provider
dartantic -a anthropic -p "Explain recursion in one sentence"

# Pipe input
echo "Summarize this: Dart is a client-optimized language" | dartantic

# Attach files
dartantic -p "Describe this image: @photo.jpg"

dartantic [global-options] [command] [command-options]

# Correct
dartantic -a openai -p "Hello"
dartantic -v chat -p "Hello"

# Incorrect - causes errors
dartantic chat -a openai -p "Hello"

# Basic chat (google is the default provider)
dartantic -p "What is the capital of France?"

# Different providers
dartantic -a openai -p "Hello from GPT-4o"
dartantic -a anthropic -p "Hello from Claude"
dartantic -a ollama -p "Hello from local Llama"

# Model strings for specific models
dartantic -a "google:gemini-2.5-flash" -p "Quick response please"
dartantic -a "openai?chat=gpt-4o&embeddings=text-embedding-3-small" -p "Hi"

# File attachments
dartantic -p "Summarize this: @document.txt"
dartantic -p "Compare these: @file1.txt and @file2.txt"
dartantic -p "What's in this image? @screenshot.png"

# Structured JSON output
dartantic -p "List 3 colors" --output-schema '{"type":"array","items":{"type":"string"}}'

# Control temperature
dartantic -t 0.9 -p "Write a creative story opening"

# Verbose mode (shows token usage)
dartantic -v -p "Hello"

# From stdin
echo "Your prompt" | dartantic
cat prompt.txt | dartantic

# Generate an image
dartantic generate --mime image/png -p "A minimalist robot logo"

# Generate to a specific directory
dartantic generate --mime image/png -p "A sunset" -o ./images/

# Generate a PDF
dartantic generate -a openai-responses --mime application/pdf \
  -p "Create a one-page report about AI trends"

# Generate CSV data
dartantic generate -a openai-responses --mime text/csv \
  -p "Sample user data with name, email, age columns"

# Generate multiple formats
dartantic generate -p @prompt.txt --mime image/jpeg --mime image/png

dartantic embed create [options] <files...>

# Create embeddings
dartantic embed create doc1.txt doc2.txt > embeddings.json

# Custom chunk settings
dartantic embed create --chunk-size 256 --chunk-overlap 50 *.txt > small-chunks.json

{
  "model": "google",
  "created": "2024-12-14T15:30:00Z",
  "chunk_size": 512,
  "chunk_overlap": 100,
  "documents": [
    {
      "file": "doc.txt",
      "chunks": [
        {
          "text": "chunk content",
          "vector": [0.1, 0.2, ...],
          "offset": 0
        }
      ]
    }
  ]
}

dartantic embed search -q <query> <embeddings.json>

# Semantic search
dartantic embed search -q "machine learning concepts" embeddings.json

# Search with scores
dartantic -v embed search -q "neural networks" embeddings.json

# Search directory of JSON files
dartantic embed search -q "API usage" ./embeddings/

{
  "query": "search term",
  "results": [
    {
      "file": "doc.txt",
      "text": "matching text...",
      "offset": 125,
      "similarity": 0.87
    }
  ]
}

# Default provider (google)
dartantic models

# Specific provider
dartantic -a openai models
dartantic -a anthropic models

Provider: Google (google)

Chat Models:
  gemini-2.5-flash
  gemini-1.5-pro

Embeddings Models:
  text-embedding-004

Media Models:
  imagen-3

# Global defaults
default_agent: coder
thinking: true
server_tools: true
chunk_size: 512
chunk_overlap: 100

agents:
  # Simple agent with just model
  default:
    model: google

  # Coding assistant
  coder:
    model: anthropic:claude-sonnet-4-20250514
    system: |
      You are an expert software engineer.
      Write clean, well-documented code.

  # Fast responses
  quick:
    model: google:gemini-2.5-flash
    thinking: false

  # Entity extraction with structured output
  extractor:
    model: openai:gpt-4o
    output_schema:
      type: object
      properties:
        entities:
          type: array
          items:
            type: object
            properties:
              name: { type: string }
              type: { type: string }
      required: [entities]

  # Research agent with MCP tools (remote)
  research:
    model: anthropic:claude-sonnet-4-20250514
    mcp_servers:
      - name: context7
        url: https://mcp.context7.com/mcp
        headers:
          CONTEXT7_API_KEY: "${CONTEXT7_API_KEY}"

  # Agent with local MCP server
  filesystem:
    model: google
    mcp_servers:
      - name: filesystem
        command: npx
        args: ["-y", "@anthropic/mcp-server-filesystem", "/tmp"]

  # Custom provider endpoint
  custom:
    model: openai:gpt-4o-mini
    base_url: https://api.custom-provider.com/v1
    headers:
      X-Custom-Header: value

dartantic -a coder -p "Write a binary search in Rust"
dartantic -a extractor -p "John Smith works at Acme Corp"
dartantic -a research -p "Find documentation about hooks"

agents:
  custom:
    model: openai
    headers:
      Authorization: "Bearer ${MY_API_KEY}"

# math.prompt
---
model: google
input:
  default:
    operation: add
---
Calculate: What is 5 {{operation}} 3?

# Uses default (add)
dartantic -p @math.prompt

# Override variable
dartantic -p @math.prompt operation=multiply

# Set default agent
export DARTANTIC_AGENT=anthropic

# Enable debug logging
export DARTANTIC_LOG_LEVEL=FINE

# API keys (standard pattern)
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GEMINI_API_KEY="..."