orchid-cli 1.0.1

Orchid CLI — command-line interface for the Orchid agent framework

Orchid CLI

Command-line interface for the Orchid multi-agent AI framework.

Provides terminal access to all chat operations, configuration validation, RAG indexing, and Claude Code skill generation. Mirrors the full functionality of orchid-api but runs locally with no server, Docker, or external database required (defaults to SQLite).

Installation

pip install orchid-ai orchid-cli

The orchid command is available after installation.

Quick Start

# Validate config:
orchid config validate agents.yaml

# Start an interactive chat session:
orchid chat interactive -c orchid.yml

# Send a single message:
orchid chat create -c orchid.yml -t "My Chat"
orchid chat send <chat_id> "Hello!" -c orchid.yml

Commands

Chat Management

# Create a new chat session
orchid chat create -c orchid.yml -t "My Chat Title"

# List all chat sessions
orchid chat list -c orchid.yml

# Show message history
orchid chat history <chat_id> -c orchid.yml
orchid chat history <chat_id> -c orchid.yml --limit 10

# Rename a chat
orchid chat rename <chat_id> "New Title" -c orchid.yml

# Share a chat (promote RAG data to user scope)
orchid chat share <chat_id> -c orchid.yml

# Delete a chat
orchid chat delete <chat_id> -c orchid.yml
orchid chat delete <chat_id> -c orchid.yml --force

Chat IDs support prefix matching -- type the first few characters of the UUID.

Messaging

# Send a single message and print the response
orchid chat send <chat_id> "What is LangGraph?" -c orchid.yml

# Override the LLM model
orchid chat send <chat_id> "Explain RAG" -c orchid.yml -m ollama/llama3.2

Interactive Mode

# Start a new interactive session
orchid chat interactive -c orchid.yml

# Resume an existing chat
orchid chat interactive <chat_id> -c orchid.yml

Slash commands available inside interactive mode:

Command	Purpose
/list	List all chat sessions
/switch <id>	Switch to another chat
/new [title]	Create a new chat
/history	Show last 20 messages
/rename <title>	Rename current chat
/quit	Exit interactive mode

Configuration

# Validate an agents.yaml file
orchid config validate path/to/agents.yaml

RAG Indexing

# Seed the vector store with static data
orchid index seed -c orchid.yml

# Seed for a specific tenant
orchid index seed -c orchid.yml --tenant my-tenant

Skill Generation (Claude Code)

Generate Claude Code skills from your Orchid agent configuration. Each agent and orchestrator skill becomes a Claude Code skill directory with a SKILL.md file.

# Generate skills for all agents and orchestrator skills
orchid skill generate path/to/agents.yaml

# Custom output directory
orchid skill generate path/to/agents.yaml -o .claude/skills

# Generate only specific agents/skills
orchid skill generate path/to/agents.yaml --include basketball,psychologist

# Overwrite existing skill directories
orchid skill generate path/to/agents.yaml --overwrite

# Create a zip archive for upload
orchid skill generate path/to/agents.yaml --zip

What gets converted:

Orchid Concept	Claude Code Skill
Agent prompt	Core SKILL.md instructions
Agent description	Skill frontmatter description
Built-in tools	Executable Python scripts in scripts/
Agent skills (workflows)	Step-by-step workflow instructions with script commands
Orchestrator skills	Multi-agent workflow skill
MCP servers	Noted as runtime-only (not portable)
RAG context	Noted as runtime-only (not portable)
Guardrails (global + per-agent)	Input/output rules section with actions and config

Each agent skill includes a scripts/ folder with standalone Python scripts that Claude Code can execute directly. Tools from the same source module are grouped into a single script file with a CLI wrapper that accepts --arg value arguments.

Configuration

The --config (-c) flag points to an orchid.yml file:

llm:
  model: ollama/llama3.2
agents:
  config_path: agents.yaml
rag:
  vector_backend: null      # no Qdrant needed for basic usage
storage:
  class: orchid.persistence.sqlite.SQLiteChatStorage
  dsn: ~/.orchid/chats.db

Defaults

Parameter	Default	Env Override
LLM model	ollama/llama3.2	LITELLM_MODEL
Vector backend	qdrant	VECTOR_BACKEND
Storage class	orchid.persistence.sqlite.SQLiteChatStorage	CHAT_STORAGE_CLASS
Storage DSN	~/.orchid/chats.db	CHAT_DB_DSN

Chat data is stored in SQLite at ~/.orchid/chats.db by default. The directory is created automatically on first run.

Prerequisites

• Python 3.11+
• Ollama running locally (for local LLM models): ollama pull llama3.2

Architecture

orchid_cli/
  main.py          Typer entry point -- registers sub-commands
  bootstrap.py     Shared startup: load config, build graph, init storage
  commands/
    chat.py        Full CRUD + messaging + interactive mode
    config.py      Validate agents.yaml
    index.py       Seed RAG vector store
    skill.py       Generate Claude Code skills from agents.yaml

The CLI is a thin layer that calls orchid SDK functions and displays results via Rich.

Development

pip install -e ../orchid -e ".[dev]"
orchid config validate ../examples/basketball/agents.yaml

Testing

pip install -e ".[dev]"
pytest tests/ -x
ruff check orchid_cli/

License

MIT -- see LICENSE.