Tyler CLI

Command-line interface for Tyler agent development

The Tyler CLI provides tools for creating and interacting with Tyler agents. It includes commands for scaffolding new projects and chatting with agents interactively.

Installation

The Tyler CLI is automatically installed when you install the Tyler package:

uv (recommended)

uv add slide-tyler

pip

pip install slide-tyler

After installation, the tyler command will be available in your terminal.

Commands

tyler init

Create a new Tyler agent project with all the necessary scaffolding:

tyler init my-agent
tyler init "Research Assistant" --purpose "To help with academic research"

This creates a project structure with:

• agent.py - Main agent configuration
• tyler-config.yaml - Chat configuration
• .env.example - API key template
• pyproject.toml - Project dependencies
• tools/ - Directory for custom tools
• README.md - Project documentation

tyler chat

Start an interactive chat session with an agent:

tyler chat
tyler chat --config my-agent-config.yaml
tyler chat --title "Research Session"

This launches an interactive chat where you can:

• Type messages and see streaming responses
• Use commands to manage your conversation
• Switch between different conversation threads

Configuration

Tyler Chat can be configured using a YAML or JSON file to customize the agent’s behavior, tools, and parameters.

Using a configuration file

tyler chat --config my-agent-config.yaml
# or
tyler chat -c config.json

Configuration file format

Create a tyler-config.yaml file:

# Agent Identity
name: "Tyler"
purpose: "To be a helpful AI assistant with access to various tools and capabilities."
notes: |
  - Prefer clear, concise communication
  - Use tools when appropriate to enhance responses
  - Maintain context across conversations

# Model Configuration
model_name: "gpt-4o"  # or any LiteLLM-compatible model
temperature: 0.7
max_tool_iterations: 10

# Instruction Configuration
# AGENTS.md is auto-discovered by default from this config directory upward.
# agents_md: false
# instruction_role: "developer"  # optional; default is "system"

# Tool Configuration
tools:
  # Built-in tool modules
  - "web"           # Web search and browsing
  - "files"         # File operations
  - "slack"         # Slack integration
  - "notion"        # Notion integration
  - "command_line"  # System commands
  - "image"         # Image processing
  - "audio"         # Audio processing
  
  # Custom tool files
  - "./my_custom_tools.py"
  - "~/tools/special_tool.py"

# Skills are explicit and each path must contain SKILL.md
skills:
  - "./skills/code-review"

# MCP Server Configuration (optional)
# Connect to reviewed external docs, APIs, databases.
# Use streamablehttp for remote servers, stdio for local servers,
# and sse only for legacy compatibility.
# mcp:
#   servers:
#     - name: docs
#       transport: streamablehttp
#       url: https://docs.example.com/mcp
#       include_tools: ["search"]

See the MCP Integration Guide for full MCP configuration options.

Environment variable substitution

Config files support environment variable substitution using ${VAR_NAME} syntax. This is useful for securely referencing API keys:

# Example: W&B Inference configuration
model_name: "openai/deepseek-ai/DeepSeek-R1-0528"
base_url: "https://api.inference.wandb.ai/v1"
api_key: "${WANDB_API_KEY}"  # Reads from environment
extra_headers:
  HTTP-Referer: "https://wandb.ai/my-team/my-project"

Never hardcode API keys in config files. Always use environment variables via ${VAR_NAME} syntax or store them in your .env file.

Command line options

# Specify a configuration file
tyler chat --config path/to/config.yaml

# Set an initial thread title
tyler chat --title "Research Session"

# Combine options
tyler chat -c my-config.yaml -t "Project Discussion"

Chat commands

During a chat session, you can use these special commands:

Command	Description
/help	Show available commands
/quit or /exit	Exit the chat
/new	Create a new conversation thread
/threads	List all conversation threads
/switch <id>	Switch to a different thread
/save	Save the current thread
/clear	Clear the screen

Command examples

You: /threads
╭─────────────── Threads ───────────────╮
│ 1. Research Session (2 messages)      │
│ 2. Code Review (5 messages)           │
│ 3. Project Planning (3 messages)      │
╰──────────────────────────────────────╯

You: /switch 2
Switched to thread: Code Review

You: /new
Created new thread: Untitled Thread

Features

Streaming responses

Tyler Chat displays responses in real-time as they’re generated, providing immediate feedback and a more interactive experience.

Thread persistence

Conversations are automatically saved and can be resumed later. By default, threads are stored in memory during the session.

Rich formatting

• Markdown support: Responses are rendered with proper formatting
• Syntax highlighting: Code blocks are displayed with syntax colors
• Structured output: Tables, lists, and other elements are properly formatted

Tool integration

When your agent uses tools, you’ll see real-time updates:

You: Search for the latest AI news

Agent: Let me search for the latest AI news for you.

[🔧 Using tool: web-search]

Here's what I found about the latest AI developments...

Advanced usage

Persistent storage

To enable persistent storage across sessions, set up a database:

# Set environment variable
export NARRATOR_DATABASE_URL="sqlite:///tyler_chat.db"

# Then run tyler chat
tyler chat

Custom tools

Create a Python file with your custom tools:

# my_tools.py
from lye import tool

@tool
def calculate_compound_interest(
    principal: float,
    rate: float,
    time: int,
    compounds_per_year: int = 12
) -> float:
    """Calculate compound interest"""
    amount = principal * (1 + rate/compounds_per_year) ** (compounds_per_year * time)
    return round(amount, 2)

# Export tools
TOOLS = [calculate_compound_interest]

Then reference it in your config:

tools:
  - "./my_tools.py"

Environment variables

Tyler Chat respects these environment variables:

Variable	Description
OPENAI_API_KEY	API key for OpenAI models
ANTHROPIC_API_KEY	API key for Anthropic models
WANDB_API_KEY	W&B API key (for W&B Inference or Weave tracking)
WANDB_PROJECT	W&B project for Weave tracking (optional - if not set, Weave won’t initialize)
NARRATOR_DATABASE_URL	Database URL for thread persistence

To enable Weave tracing for observability, set WANDB_PROJECT to your desired project name. If not set, the CLI runs without tracing overhead for faster startup.

Troubleshooting

Clean output mode

Tyler Chat automatically suppresses noisy output from third-party libraries. If you need to see debug information:

# Enable debug mode
TYLER_DEBUG=1 tyler chat

Common issues

“Module not found” errors: Make sure all dependencies are installed:

uv add slide-tyler[all]

API key errors: Ensure your API keys are set in environment variables or .env file

Database errors: Check your NARRATOR_DATABASE_URL is correctly formatted

Examples

Research assistant

# research-assistant-config.yaml
name: "Research Assistant"
purpose: "To help with in-depth research and analysis"
model_name: "gpt-4o"
tools:
  - "web"
  - "files"
notes: |
  - Always cite sources
  - Create organized reports
  - Fact-check information

tyler chat -c research-assistant-config.yaml -t "Climate Research"

Code helper

# code-helper-config.yaml
name: "Code Helper"
purpose: "To assist with programming tasks"
temperature: 0.3  # Lower temperature for more consistent code
tools:
  - "files"
  - "command_line"
notes: |
  - Write clean, well-commented code
  - Follow best practices
  - Include error handling

Quick project setup

Use tyler init to scaffold a complete project:

# Create a new research assistant project
tyler init research-bot --purpose "To help with academic research and paper analysis"
cd research-bot

# Set up environment
cp .env.example .env
# Edit .env with your API keys

# Start chatting with your configured agent
tyler chat --config tyler-config.yaml

Next steps

Build custom agents

Learn to create agents programmatically

Add custom tools

Extend agent capabilities with custom tools

Deploy to Slack

Run your agent as a Slack bot

Advanced patterns

Explore advanced agent patterns