🔥 Helios Engine - LLM Agent Framework

Helios Engine is a powerful and flexible Rust framework for building LLM-powered agents with tool support, streaming chat capabilities, and easy configuration management. Create intelligent agents that can interact with users, call tools, and maintain conversation context - with both online and offline local model support.

Features

Agent System: Create multiple agents with different personalities and capabilities
Tool Registry: Extensible tool system for adding custom functionality
Chat Management: Built-in conversation history and session management
Streaming Support: Real-time response streaming with thinking tag detection
Local Model Support: Run local models offline using llama.cpp with HuggingFace integration
LLM Support: Compatible with OpenAI API, any OpenAI-compatible API, and local models
Async/Await: Built on Tokio for high-performance async operations
Type-Safe: Leverages Rust's type system for safe and reliable code
Extensible: Easy to add custom tools and extend functionality
Thinking Tags: Automatic detection and display of model reasoning process
Dual Mode Support: Auto, online (remote API), and offline (local) modes
Clean Output: Suppresses verbose debugging in offline mode for clean user experience
CLI & Library: Use as both a command-line tool and a Rust library crate

Installation
Quick Start
CLI Usage
Configuration
Local Inference Setup
Architecture
Usage Examples
Creating Custom Tools
API Documentation
Project Structure
Examples
Contributing
License

Installation

Helios Engine can be used both as a command-line tool and as a library crate in your Rust projects.

As a CLI Tool (Recommended for Quick Start)

Install globally using Cargo (once published):

cargo install helios-engine

Then use anywhere:

# Initialize configuration
helios-engine init

# Start interactive chat (default command)
helios-engine
# or explicitly
helios-engine chat

# Ask a quick question
helios-engine ask "What is Rust?"

# Get help
helios-engine --help

#  NEW: Use offline mode with local models (no internet required)
helios-engine --mode offline chat

# Use online mode (forces remote API usage)
helios-engine --mode online chat

# Auto mode (uses local if configured, otherwise remote)
helios-engine --mode auto chat

# Verbose logging for debugging
helios-engine --verbose chat

# Custom system prompt
helios-engine chat --system-prompt "You are a Python expert"

# One-off question with custom config
helios-engine --config /path/to/config.toml ask "Calculate 15 * 7"

As a Library Crate

Add Helios-Engine to your Cargo.toml:

[dependencies]
helios-engine = "0.1.9"
tokio = { version = "1.35", features = ["full"] }

Or use a local path during development:

[dependencies]
helios-engine = { path = "../helios" }
tokio = { version = "1.35", features = ["full"] }

Build from Source

git clone https://github.com/Ammar-Alnagar/Helios-Engine.git
cd Helios-Engine
cargo build --release

# Install locally
cargo install --path .

Quick Start

Using as a Library Crate

The simplest way to use Helios Engine is to call LLM models directly:

use helios_engine::{LLMClient, ChatMessage, llm::LLMProviderType};
use helios_engine::config::LLMConfig;

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    // Configure the LLM
    let llm_config = LLMConfig {
        model_name: "gpt-3.5-turbo".to_string(),
        base_url: "https://api.openai.com/v1".to_string(),
        api_key: std::env::var("OPENAI_API_KEY").unwrap(),
        temperature: 0.7,
        max_tokens: 2048,
    };

    // Create client with remote provider type
    let client = LLMClient::new(LLMProviderType::Remote(llm_config)).await?;

    // Make a call
    let messages = vec![
        ChatMessage::system("You are a helpful assistant."),
        ChatMessage::user("What is the capital of France?"),
    ];

    let response = client.chat(messages, None).await?;
    println!("Response: {}", response.content);

    Ok(())
}

** For detailed examples of using Helios Engine as a crate, see Using as a Crate Guide **

Using Offline Mode with Local Models

Run models locally without internet connection:

use helios_engine::{LLMClient, ChatMessage, llm::LLMProviderType};
use helios_engine::config::LocalConfig;

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    // Configure local model
    let local_config = LocalConfig {
        huggingface_repo: "unsloth/Qwen3-0.6B-GGUF".to_string(),
        model_file: "Qwen3-0.6B-Q4_K_M.gguf".to_string(),
        temperature: 0.7,
        max_tokens: 2048,
    };

    // Create client with local provider
    let client = LLMClient::new(LLMProviderType::Local(local_config)).await?;

    let messages = vec![
        ChatMessage::system("You are a helpful AI assistant."),
        ChatMessage::user("What is Rust programming?"),
    ];

    let response = client.chat(messages, None).await?;
    println!("Response: {}", response.content);

    Ok(())
}

Note: First run downloads the model. Subsequent runs use the cached model.

Using with Agent System

For more advanced use cases with tools and persistent conversation:

1. Configure Your LLM

Create a config.toml file (supports both remote and local):

[llm]
model_name = "gpt-3.5-turbo"
base_url = "https://api.openai.com/v1"
api_key = "your-api-key-here"
temperature = 0.7
max_tokens = 2048

# Optional: Add local configuration for offline mode
[local]
huggingface_repo = "unsloth/Qwen3-0.6B-GGUF"
model_file = "Qwen3-0.6B-Q4_K_M.gguf"
temperature = 0.7
max_tokens = 2048

2. Create Your First Agent

use helios_engine::{Agent, Config, CalculatorTool};

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    // Load configuration
    let config = Config::from_file("config.toml")?;

    // Create an agent with tools
    let mut agent = Agent::builder("HeliosAgent")
        .config(config)
        .system_prompt("You are a helpful AI assistant.")
        .tool(Box::new(CalculatorTool))
        .build()
        .await?;

    // Chat with the agent
    let response = agent.chat("What is 15 * 7?").await?;
    println!("Agent: {}", response);

    Ok(())
}

3. Run the Interactive Demo

cargo run

CLI Usage

Helios Engine provides a powerful command-line interface with multiple modes and options:

Interactive Chat Mode

Start an interactive chat session:

# Default chat session
helios-engine

# With custom system prompt
helios-engine chat --system-prompt "You are a helpful coding assistant"

# With custom max iterations for tool calls
helios-engine chat --max-iterations 10

# With verbose logging for debugging
helios-engine --verbose chat

One-off Questions

Ask a single question without interactive mode:

# Ask a single question
helios-engine ask "What is the capital of France?"

# Ask with custom config file
helios-engine --config /path/to/config.toml ask "Calculate 123 * 456"

Configuration Management

Initialize and manage configuration:

# Create a new configuration file
helios-engine init

# Create config in custom location
helios-engine init --output ~/.helios/config.toml

Mode Selection

Choose between different operation modes:

# Auto mode (uses local if configured, otherwise remote API)
helios-engine --mode auto chat

# Online mode (forces remote API usage)
helios-engine --mode online chat

# Offline mode (uses local models only)
helios-engine --mode offline chat

Interactive Commands

During an interactive session, use these commands:

exit or quit - Exit the chat session
clear - Clear conversation history
history - Show conversation history
help - Show help message

Configuration

Helios Engine uses TOML for configuration. You can configure either remote API access or local model inference with the dual LLMProviderType system.

Remote API Configuration (Default)

[llm]
# The model name (e.g., gpt-3.5-turbo, gpt-4, claude-3, etc.)
model_name = "gpt-3.5-turbo"

# Base URL for the API (OpenAI or compatible)
base_url = "https://api.openai.com/v1"

# Your API key
api_key = "your-api-key-here"

# Temperature for response generation (0.0 - 2.0)
temperature = 0.7

# Maximum tokens in response
max_tokens = 2048

Local Model Configuration (Offline Mode with llama.cpp)

[llm]
# Remote config still needed for auto mode fallback
model_name = "gpt-3.5-turbo"
base_url = "https://api.openai.com/v1"
api_key = "your-api-key-here"
temperature = 0.7
max_tokens = 2048

# Local model configuration for offline mode
[local]
# HuggingFace repository and model file
huggingface_repo = "unsloth/Qwen3-0.6B-GGUF"
model_file = "Qwen3-0.6B-Q4_K_M.gguf"

# Local model settings
temperature = 0.7
max_tokens = 2048

Auto Mode Configuration (Remote + Local)

For maximum flexibility, configure both remote and local models to enable auto mode:

[llm]
model_name = "gpt-3.5-turbo"
base_url = "https://api.openai.com/v1"
api_key = "your-api-key-here"
temperature = 0.7
max_tokens = 2048

# Local model as fallback
[local]
huggingface_repo = "unsloth/Qwen3-0.6B-GGUF"
model_file = "Qwen3-0.6B-Q4_K_M.gguf"
temperature = 0.7
max_tokens = 2048

Supported LLM Providers

Helios Engine supports both remote APIs and local model inference:

Remote APIs (Online Mode)

Helios Engine works with any OpenAI-compatible API:

OpenAI: https://api.openai.com/v1
Azure OpenAI: https://your-resource.openai.azure.com/openai/deployments/your-deployment
Local Models (LM Studio): http://localhost:1234/v1
Ollama with OpenAI compatibility: http://localhost:11434/v1
Any OpenAI-compatible API

Local Models (Offline Mode)

Run models locally using llama.cpp without internet connection:

GGUF Models: Compatible with all GGUF format models from HuggingFace
Automatic Download: Models are downloaded automatically from HuggingFace
GPU Acceleration: Uses GPU if available (via llama.cpp)
Clean Output: Suppresses verbose debugging for clean user experience
Popular Models: Works with Qwen, Llama, Mistral, and other GGUF models

Supported Model Sources:

HuggingFace Hub repositories
Local GGUF files
Automatic model caching

Local Inference Setup

Helios Engine supports running large language models locally using llama.cpp through the LLMProviderType system, providing privacy, offline capability, and no API costs.

Prerequisites

HuggingFace Account: Sign up at huggingface.co (free)

HuggingFace CLI: Install the CLI tool:

pip install huggingface_hub
huggingface-cli login  # Login with your token

Setting Up Local Models

Find a GGUF Model: Browse HuggingFace Models for compatible models

Update Configuration: Add local model config to your config.toml:

[local]
huggingface_repo = "unsloth/Qwen3-0.6B-GGUF"
model_file = "Qwen3-0.6B-Q4_K_M.gguf"
temperature = 0.7
max_tokens = 2048

Run in Offline Mode:

# First run downloads the model
helios-engine --mode offline ask "Hello world"

# Subsequent runs use cached model
helios-engine --mode offline chat

Recommended Models

Model	Size	Use Case	Repository
Qwen3-0.6B	~400MB	Fast, good quality	`unsloth/Qwen3-0.6B-GGUF`
Llama-3.2-1B	~700MB	Balanced performance	`unsloth/Llama-3.2-1B-Instruct-GGUF`
Mistral-7B	~4GB	High quality	`TheBloke/Mistral-7B-Instruct-v0.1-GGUF`

Performance & Features

GPU Acceleration: Models automatically use GPU if available via llama.cpp's n_gpu_layers parameter
Model Caching: Downloaded models are cached locally (~/.cache/huggingface)
Memory Usage: Larger models need more RAM/VRAM
First Run: Initial model download may take time depending on connection
Clean Output Mode: Suppresses verbose debugging from llama.cpp for clean user experience

Streaming Support with Local Models

While streaming is available for remote models, local models currently provide full responses. The LLMClient automatically handles both streaming (remote) and non-streaming (local) modes consistently through the same API.

Architecture

System Overview

graph TB
    User[User] -->|Input| Agent[Agent]
    Agent -->|Messages| LLM[LLM Client]
    Agent -->|Tool Calls| Registry[Tool Registry]
    Registry -->|Execute| Tools[Tools]
    Tools -->|Results| Agent
    LLM -->|Response| Agent
    Agent -->|Output| User
    Config[Config TOML] -->|Load| Agent

    style Agent fill:#4CAF50
    style LLM fill:#2196F3
    style Registry fill:#FF9800
    style Tools fill:#9C27B0

Component Architecture

classDiagram
    class Agent {
        +name: String
        +llm_client: LLMClient
        +tool_registry: ToolRegistry
        +chat_session: ChatSession
        +chat(message) ChatMessage
        +register_tool(tool) void
        +clear_history() void
    }

    class LLMClient {
        +provider: LLMProvider
        +provider_type: LLMProviderType
        +chat(messages, tools) ChatMessage
        +chat_stream(messages, tools, callback) ChatMessage
        +generate(request) LLMResponse
    }

    class ToolRegistry {
        +tools: HashMap
        +register(tool) void
        +execute(name, args) ToolResult
        +get_definitions() Vec
    }

    class Tool {
        <<interface>>
        +name() String
        +description() String
        +parameters() HashMap
        +execute(args) ToolResult
    }

    class ChatSession {
        +messages: Vec
        +system_prompt: Option
        +add_message(msg) void
        +clear() void
    }

    class Config {
        +llm: LLMConfig
        +from_file(path) Config
        +save(path) void
    }

    Agent --> LLMClient
    Agent --> ToolRegistry
    Agent --> ChatSession
    Agent --> Config
    ToolRegistry --> Tool
    Tool <|-- CalculatorTool
    Tool <|-- EchoTool
    Tool <|-- CustomTool

Agent Execution Flow

sequenceDiagram
    participant User
    participant Agent
    participant LLM
    participant ToolRegistry
    participant Tool

    User->>Agent: Send Message
    Agent->>Agent: Add to Chat History

    loop Until No Tool Calls
        Agent->>LLM: Send Messages + Tool Definitions
        LLM->>Agent: Response (with/without tool calls)

        alt Has Tool Calls
            Agent->>ToolRegistry: Execute Tool
            ToolRegistry->>Tool: Call with Arguments
            Tool->>ToolRegistry: Return Result
            ToolRegistry->>Agent: Tool Result
            Agent->>Agent: Add Tool Result to History
        else No Tool Calls
            Agent->>User: Return Final Response
        end
    end

Tool Execution Pipeline

flowchart LR
    A[User Request] --> B{LLM Decision}
    B -->|Need Tool| C[Get Tool Definition]
    C --> D[Parse Arguments]
    D --> E[Execute Tool]
    E --> F[Format Result]
    F --> G[Add to Context]
    G --> B
    B -->|No Tool Needed| H[Return Response]
    H --> I[User]

    style B fill:#FFD700
    style E fill:#4CAF50
    style H fill:#2196F3

Usage Examples

Basic Chat

use helios_engine::{Agent, Config};

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    let config = Config::from_file("config.toml")?;

    let mut agent = Agent::builder("Assistant")
        .config(config)
        .system_prompt("You are a helpful assistant.")
        .build()?;

    let response = agent.chat("Hello!").await?;
    println!("{}", response);

    Ok(())
}

Agent with Built-in Tools

use helios_engine::{Agent, Config, CalculatorTool, EchoTool};

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    let config = Config::from_file("config.toml")?;

    let mut agent = Agent::builder("ToolAgent")
        .config(config)
        .system_prompt("You have access to tools. Use them wisely.")
        .tool(Box::new(CalculatorTool))
        .tool(Box::new(EchoTool))
        .max_iterations(5)
        .build()?;

    // The agent will automatically use the calculator
    let response = agent.chat("What is 123 * 456?").await?;
    println!("{}", response);

    Ok(())
}

Multiple Agents

use helios_engine::{Agent, Config};

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    let config = Config::from_file("config.toml")?;

    let mut poet = Agent::builder("Poet")
        .config(config.clone())
        .system_prompt("You are a creative poet.")
        .build()?;

    let mut scientist = Agent::builder("Scientist")
        .config(config)
        .system_prompt("You are a knowledgeable scientist.")
        .build()?;

    let poem = poet.chat("Write a haiku about code").await?;
    let fact = scientist.chat("Explain quantum physics").await?;

    println!("Poet: {}\n", poem);
    println!("Scientist: {}", fact);

    Ok(())
}

Streaming Chat (Direct LLM Usage)

Use streaming to receive responses in real-time:

use helios_engine::{LLMClient, ChatMessage, llm::LLMProviderType};
use helios_engine::config::LLMConfig;

#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    let llm_config = LLMConfig {
        model_name: "gpt-3.5-turbo".to_string(),
        base_url: "https://api.openai.com/v1".to_string(),
        api_key: std::env::var("OPENAI_API_KEY").unwrap(),
        temperature: 0.7,
        max_tokens: 2048,
    };

    // Create client with remote provider type (streaming enabled)
    let client = LLMClient::new(LLMProviderType::Remote(llm_config)).await?;

    let messages = vec![
        ChatMessage::system("You are a helpful assistant that responds concisely."),
        ChatMessage::user("Write a short poem about programming."),
    ];

    println!("🤖: ");
    let response = client
        .chat_stream(messages, None, |chunk| {
            print!("{}", chunk);
            std::io::stdout().flush().unwrap(); // For immediate output
        })
        .await?;
    println!(); // New line after streaming completes

    Ok(())
}

Creating Custom Tools

Implement the Tool trait to create custom tools:

use async_trait::async_trait;
use helios_engine::{Tool, ToolParameter, ToolResult};
use serde_json::Value;
use std::collections::HashMap;

struct WeatherTool;

#[async_trait]
impl Tool for WeatherTool {
    fn name(&self) -> &str {
        "get_weather"
    }

    fn description(&self) -> &str {
        "Get the current weather for a location"
    }

    fn parameters(&self) -> HashMap<String, ToolParameter> {
        let mut params = HashMap::new();
        params.insert(
            "location".to_string(),
            ToolParameter {
                param_type: "string".to_string(),
                description: "City name".to_string(),
                required: Some(true),
            },
        );
        params
    }

    async fn execute(&self, args: Value) -> helios_engine::Result<ToolResult> {
        let location = args["location"].as_str().unwrap_or("Unknown");

        // Your weather API logic here
        let weather = format!("Weather in {}: Sunny, 72°F", location);

        Ok(ToolResult::success(weather))
    }
}

// Use your custom tool
#[tokio::main]
async fn main() -> helios_engine::Result<()> {
    let config = Config::from_file("config.toml")?;

    let mut agent = Agent::builder("WeatherAgent")
        .config(config)
        .tool(Box::new(WeatherTool))
        .build()?;

    let response = agent.chat("What's the weather in Tokyo?").await?;
    println!("{}", response);

    Ok(())
}

API Documentation

Core Types

`Agent`

The main agent struct that manages conversation and tool execution.

Methods:

builder(name) - Create a new agent builder
chat(message) - Send a message and get a response
register_tool(tool) - Add a tool to the agent
clear_history() - Clear conversation history
set_system_prompt(prompt) - Set the system prompt
set_max_iterations(max) - Set maximum tool call iterations

`Config`

Configuration management for LLM settings.

Methods:

from_file(path) - Load config from TOML file
default() - Create default configuration
save(path) - Save config to file

`LLMClient`

Client for interacting with LLM providers (remote or local).

Methods:

new(provider_type) - Create client with LLMProviderType (Remote or Local)
chat(messages, tools) - Send messages and get response
chat_stream(messages, tools, callback) - Send messages and stream response with callback function
generate(request) - Low-level generation method

`LLMProviderType`

Enumeration for different LLM provider types.

Variants:

Remote(LLMConfig) - For remote API providers (OpenAI, Azure, etc.)
Local(LocalConfig) - For local llama.cpp models

`ToolRegistry`

Manages and executes tools.

Methods:

new() - Create empty registry
register(tool) - Register a new tool
execute(name, args) - Execute a tool by name
get_definitions() - Get all tool definitions
list_tools() - List registered tool names

`ChatSession`

Manages conversation history.

Methods:

new() - Create new session
with_system_prompt(prompt) - Set system prompt
add_message(message) - Add message to history
clear() - Clear all messages

Built-in Tools

`CalculatorTool`

Performs basic arithmetic operations.

Parameters:

expression (string, required): Mathematical expression

Example:

agent.tool(Box::new(CalculatorTool));

`EchoTool`

Echoes back a message.

Parameters:

message (string, required): Message to echo

Example:

agent.tool(Box::new(EchoTool));

Project Structure

helios/
├── Cargo.toml              # Project configuration
├── README.md               # This file
├── config.example.toml     # Example configuration
├── .gitignore             # Git ignore rules
│
├── src/
│   ├── lib.rs             # Library entry point
│   ├── main.rs            # Binary entry point (interactive demo)
│   ├── agent.rs           # Agent implementation
│   ├── llm.rs             # LLM client and provider
│   ├── tools.rs           # Tool system and built-in tools
│   ├── chat.rs            # Chat message and session types
│   ├── config.rs          # Configuration management
│   └── error.rs           # Error types
│
├── docs/
│   ├── API.md                    # API reference
│   ├── QUICKSTART.md             # Quick start guide
│   ├── TUTORIAL.md               # Detailed tutorial
│   └── USING_AS_CRATE.md         # Using Helios as a library
│
└── examples/
    ├── basic_chat.rs             # Simple chat example
    ├── agent_with_tools.rs       # Tool usage example
    ├── custom_tool.rs            # Custom tool implementation
    ├── multiple_agents.rs        # Multiple agents example
    └── direct_llm_usage.rs       # Direct LLM client usage

Module Overview

helios-engine/
│
├──  agent           - Agent system and builder pattern
├──  llm             - LLM client and API communication
├── ️ tools           - Tool registry and implementations
├──  chat            - Chat messages and session management
├──  config          - TOML configuration loading/saving
└──  error           - Error types and Result alias

Examples

Run the included examples:

# Basic chat
cargo run --example basic_chat

# Agent with tools
cargo run --example agent_with_tools

# Custom tool
cargo run --example custom_tool

# Multiple agents
cargo run --example multiple_agents

Testing

Run tests:

cargo test

Run with logging:

RUST_LOG=debug cargo run

🔍 Advanced Features

Custom LLM Providers

Implement the LLMProvider trait for custom backends:

use async_trait::async_trait;
use helios_engine::{LLMProvider, LLMRequest, LLMResponse};

struct CustomProvider;

#[async_trait]
impl LLMProvider for CustomProvider {
    async fn generate(&self, request: LLMRequest) -> helios_engine::Result<LLMResponse> {
        // Your custom implementation
        todo!()
    }
}

Tool Chaining

Agents automatically chain tool calls:

// The agent can use multiple tools in sequence
let response = agent.chat(
    "Calculate 10 * 5, then echo the result"
).await?;

Thinking Tags Display

Helios Engine automatically detects and displays thinking tags from LLM responses:

The CLI displays thinking tags with visual indicators: 💭 [Thinking...]
Streaming responses show thinking tags in real-time
Supports both <thinking> and <think> tag formats
In offline mode, thinking tags are processed and removed from final output

Conversation Context

Maintain conversation history:

let mut agent = Agent::new("Assistant", config);

agent.chat("My name is Alice").await?;
agent.chat("What is my name?").await?; // Agent remembers: "Alice"

Clean Output Mode

In offline mode, Helios Engine suppresses all verbose debugging output from llama.cpp:

No model loading messages
No layer information display
No verbose internal operations
Clean, user-focused experience during local inference

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

Clone the repository:

git clone https://github.com/yourusername/helios.git
cd helios

Build the project:

cargo build

Run tests:

cargo test

Format code:

cargo fmt

Check for issues:

cargo clippy

License

This project is licensed under the MIT License - see the LICENSE file for details.

Made with ❤️ in Rust

helios-engine 0.2.1

🔥 Helios Engine - LLM Agent Framework

Features

Table of Contents

Installation

As a CLI Tool (Recommended for Quick Start)

As a Library Crate

Build from Source

Quick Start

Using as a Library Crate

Using Offline Mode with Local Models

Using with Agent System

1. Configure Your LLM

2. Create Your First Agent

3. Run the Interactive Demo

CLI Usage

Interactive Chat Mode

One-off Questions

Configuration Management

Mode Selection

Interactive Commands

Configuration

Remote API Configuration (Default)

Local Model Configuration (Offline Mode with llama.cpp)

Auto Mode Configuration (Remote + Local)

Supported LLM Providers

Remote APIs (Online Mode)

Local Models (Offline Mode)

Local Inference Setup

Prerequisites

Setting Up Local Models

Recommended Models

Performance & Features

Streaming Support with Local Models

Architecture

System Overview

Component Architecture

Agent Execution Flow

Tool Execution Pipeline

Usage Examples

Basic Chat

Agent with Built-in Tools

Multiple Agents

Streaming Chat (Direct LLM Usage)

Creating Custom Tools

API Documentation

Core Types

Agent

Config

LLMClient

LLMProviderType

ToolRegistry

ChatSession

Built-in Tools

CalculatorTool

EchoTool

Project Structure

Module Overview

Examples

Testing

🔍 Advanced Features

Custom LLM Providers

Tool Chaining

Thinking Tags Display

Conversation Context

Clean Output Mode

Contributing

Development Setup

License

`Agent`

`Config`

`LLMClient`

`LLMProviderType`

`ToolRegistry`

`ChatSession`

`CalculatorTool`

`EchoTool`