Slash Commands

Interactive slash commands for PraisonAI CLI

Slash Commands

PraisonAI CLI provides interactive slash commands for quick actions during your AI coding sessions. Inspired by Gemini CLI, Codex CLI, and Claude Code, these commands give you powerful control without leaving the terminal.

Overview

Slash commands start with / and provide quick access to common operations in interactive mode.

❯ /help
❯ /tools
❯ /clear
❯ /exit

Available Commands (Interactive Mode)

When using praisonai chat, these commands are available:

Command	Description
/help	Show available commands and features
/exit	Exit interactive mode
/quit	Exit interactive mode (alias)
/clear	Clear the screen
/tools	List available tools
/profile	Toggle profiling (show timing breakdown)
/model [name]	Show or change current model
/stats	Show session statistics (tokens, cost)
/compact	Compress conversation history
/undo	Undo last response
/queue	Show queued messages
/queue clear	Clear the message queue
/queue remove N	Remove message at index N

Built-in Tools

Interactive mode includes 5 built-in tools that the AI can use:

Tool	Description	Risk Level
read_file	Read contents of a file	Low
write_file	Write content to a file	High (requires approval)
list_files	List files in a directory	Low
execute_command	Run shell commands	Critical (requires approval)
internet_search	Search the web	Low

❯ /tools
Available tools: 5
  • read_file
  • write_file
  • list_files
  • execute_command
  • internet_search

Usage Examples

Starting Interactive Mode

# Start interactive mode
praisonai chat

# Or use short flag
praisonai -i

Using Help

❯ /help

Commands:
  /help          - Show this help
  /exit          - Exit interactive mode
  /clear         - Clear screen
  /tools         - List available tools
  /profile       - Toggle profiling (show timing breakdown)
  /model [name]  - Show or change current model
  /stats         - Show session statistics (tokens, cost)
  /compact       - Compress conversation history
  /undo          - Undo last response
  /queue         - Show queued messages
  /queue clear   - Clear message queue

@ Mentions:
  @file.txt      - Include file content in prompt
  @src/          - Include directory listing

Features:
  • File operations (read, write, list)
  • Shell command execution
  • Web search
  • Context compression for long sessions
  • Queue messages while agent is processing

Listing Tools

❯ /tools
Available tools: 5
  • read_file
  • write_file
  • list_files
  • execute_command
  • internet_search

Using Tools via Natural Language

# List files
❯ list files in current folder
Here are the files: README.md, main.py, config.yaml

# Read a file
❯ read the contents of README.md
The file contains: # My Project...

# Search the web
❯ search the web for latest AI news
Here are the results from web search...

Python API

You can also use slash commands programmatically:

from praisonai.cli.features import SlashCommandHandler

# Create handler
handler = SlashCommandHandler()

# Check if input is a command
if handler.is_command("/help"):
    result = handler.execute("/help")
    print(result)

# Get completions for auto-complete
completions = handler.get_completions("/he")
# Returns: ["/help"]

Custom Commands

File-based commands in .praisonai/commands/*.md auto-register in interactive mode with kind=CommandKind.CUSTOM. Disable discovery with SlashCommandHandler(discover_custom=False). See Custom Agents & Commands.

Unified Command Registry

Inside praisonai code (and the REPL / async TUI) a single CommandRegistry aggregates every command source into one /name namespace — built-ins, your .praisonai/commands/*.md files, skills, MCP prompts, and pip-installed command packs all appear together.

Source	Where commands come from
BuiltinCommandSource	Built-in /help, /undo, /sessions, etc.
CustomCommandSource	.praisonai/commands/*.md files (same path as praisonai run --command)
SkillCommandSource	Skills exposed as /name
MCPPromptCommandSource	MCP prompts exposed as /name
praisonai.commands entry point	Any pip-installed command pack

Custom commands work inside praisonai code

Create .praisonai/commands/mydeploy.md:

Deploy to {{env}}: run tests, build the image, push to registry.

Then invoke it inside the interactive session:

> /mydeploy staging

This runs the exact interpolated template that praisonai run --command mydeploy staging would — full parity between interactive and CLI invocation.

/help lists every discovered command and command list --all shows the same unified namespace. Later sources override earlier ones on a name collision, matching user/project override semantics.

Ship a command pack as a pip package

Register a command source in the praisonai.commands entry-point group in pyproject.toml:

[project.entry-points."praisonai.commands"]
my_commands = "my_package.commands:make_source"

The registry discovers it on start — no user code needed. A broken pack is logged and skipped, never taking down the registry.

Register programmatic slash commands:

from praisonai.cli.features.slash_commands import (
    SlashCommand, SlashCommandHandler, CommandKind
)

# Define custom command
def my_command(args, context):
    return {"type": "custom", "message": f"Args: {args}"}

custom_cmd = SlashCommand(
    name="mycommand",
    description="My custom command",
    handler=my_command,
    kind=CommandKind.ACTION,
    aliases=["mc"]
)

# Register it
handler = SlashCommandHandler()
handler.register(custom_cmd)

# Use it
result = handler.execute("/mycommand arg1 arg2")

Command Context

Provide context for commands that need session data:

from praisonai.cli.features.slash_commands import CommandContext

# Create context with session data
context = CommandContext(
    total_tokens=5000,
    total_cost=0.015,
    prompt_count=10,
    current_model="gpt-4o",
    session_start_time=time.time() - 300
)

# Set context on handler
handler.set_context(context)

# Now /cost will show real data
result = handler.execute("/cost")

Integration with Interactive Mode

Slash commands are automatically available in interactive mode:

praisonai chat

>>> Hello, help me with my code
[AI responds...]

>>> /cost
Session: abc12345
Tokens: 1,500
Cost: $0.0045

>>> /model gpt-4o-mini
Model changed to: gpt-4o-mini

>>> /exit
Goodbye!

Command Reference

/help

Show help information.

/help              # Show all commands
/help <command>    # Show help for specific command

/cost

Display session cost and token statistics.

/cost              # Show full statistics

/model

Manage the AI model.

/model             # Show current model
/model <name>      # Change to specified model

/plan

Create an execution plan for a task.

/plan              # Show current plan
/plan <task>       # Create plan for task

/diff

Show git diff of current changes.

/diff              # Show all changes
/diff --staged     # Show staged changes only

/commit

Commit changes with an AI-generated message.

/commit            # Auto-generate commit message
/commit "message"  # Use custom message

/profile

Toggle profiling to see timing breakdown.

/profile           # Toggle profiling on/off

When enabled, shows timing after each response:

─── Profiling ───
Import:      0.1ms
Agent setup: 0.3ms
LLM call:    1,234.5ms
Display:     15.2ms
Total:       1,250.1ms

/stats

Show session statistics.

/stats             # Show token usage and cost

Output:

Session Statistics
  Model:          gpt-4o-mini
  Requests:       5
  Input tokens:   1,234
  Output tokens:  2,567
  Total tokens:   3,801
  Estimated cost: $0.0023
  History turns:  10

/compact

Compress conversation history to save tokens.

/compact           # Summarize older history

This command:

• Keeps the last 2 conversation turns intact
• Summarizes older turns using the LLM
• Reduces token usage for long sessions

/undo

Undo the last conversation turn.

/undo              # Remove last user prompt and AI response

/queue

Manage the message queue. Queue messages while the AI agent is processing and they’ll be executed in order.

/queue             # Show all queued messages
/queue clear       # Clear the entire queue
/queue remove N    # Remove message at index N

Output when messages are queued:

❯ /queue
⏳ Processing...

Queued Messages (2):
  0. ↳ Add docstrings to the function
  1. ↳ Create unit tests

Use /queue clear to clear, /queue remove N to remove

Type new messages while the agent is processing. They’ll be queued and executed automatically in FIFO order.

Best Practices

1. Use aliases - /h is faster than /help
2. Check costs regularly - Use /cost to monitor spending
3. Plan before executing - Use /plan for complex tasks
4. Commit frequently - Use /commit after each logical change

Related Features

• Message Queue - Full message queue documentation
• Interactive TUI - Full interactive terminal interface
• Cost Tracking - Detailed cost monitoring
• Git Integration - Git operations