groqqy 2.2.0

Clean, composable micro agentic bot powered by Groq

Groqqy 🤖

Clean, composable micro agentic bot powered by Groq

Ultra-fast, ultra-cheap, and truly agentic. Groqqy is a multi-step reasoning agent that reads files, runs commands, searches content, and chains tool calls to complete complex tasks—all with production-ready code that's perfect for learning.

Why Groqqy?

• ⚡ Blazing Fast: 460+ tokens/sec (11x faster than standard inference)
• 💰 Ultra Cheap: $0.00002-$0.00006 per interaction (300x cheaper than GPT-4)
• 🧠 Truly Agentic: Multi-step reasoning loop (THINK → ACT → OBSERVE)
• 🛠️ Tool-Capable: Execute local and platform tools with automatic chaining
• 🧩 Composable: Mix and match components (Agent, Tools, Strategies, etc.)
• 📚 Teaching-Friendly: Clean, readable code (<200 lines per file) perfect for learning agentic AI
• 📝 Export Ready: Save conversations to markdown/HTML with full tool call visibility

What's New in v2.1.0

Documentation & Export Features:

• 📝 Conversation Export: Export full conversations to markdown/HTML with tool call details
• 🎓 Self-Discovery: Agents can autonomously learn new tools via minimal seed prompts
• 🧪 Container Testing: Reproducible testing infrastructure with Podman
• 📚 Documentation Reorganization: Clean structure with guides and examples
• 🧹 Project Cleanup: Professional structure, organized tests, comprehensive examples

See CHANGELOG.md for full history.

Installation

# Clone the repository
git clone https://github.com/scottsen/groqqy.git
cd groqqy

# Install in development mode (recommended)
pip install -e .

# Or install directly
pip install .

Requirements: Python 3.8+ and a Groq API key (free at console.groq.com)

Setup:

export GROQ_API_KEY="your-api-key-here"

Quick Start

Interactive Chat

groqqy

Groqqy 🤖 (llama-3.1-8b-instant)
Type 'help' for commands, 'exit' to quit

You: Find all Python files in the current directory
Groqqy: I'll search for Python files...
        [Searches, finds files, reports results]

You: Read the first one and summarize it
Groqqy: [Reads file, provides summary]

You: export markdown my_session.md
✅ Conversation exported to my_session.md

Programmatic Use

from groqqy import Groqqy

# Create bot
bot = Groqqy()

# Simple chat
response, cost = bot.chat("Hello! What can you do?")
print(response)
print(f"Cost: ${cost:.6f}")

# Agentic task (agent chains tools automatically)
response, cost = bot.chat("Find all .py files and count lines in each")
print(response)
# Agent will: search_files("*.py") → read_file(each) → count → report

# Export conversation
bot.save_conversation("session.html")  # Auto-styled HTML
bot.save_conversation("session.md")     # Clean markdown

Custom Tools

from groqqy import Groqqy

def get_weather(city: str) -> str:
    """Get current weather for a city."""
    # Your implementation
    return f"Weather in {city}: Sunny, 72°F"

def calculate_tip(bill: float, percent: float = 15.0) -> str:
    """Calculate tip amount for a bill."""
    tip = bill * (percent / 100)
    return f"Tip: ${tip:.2f}, Total: ${bill + tip:.2f}"

# Just pass functions - auto-registration!
bot = Groqqy(tools=[get_weather, calculate_tip])

response, cost = bot.chat("What's the weather in San Francisco?")
# Agent automatically calls get_weather("San Francisco")

Core Features

🧠 Agentic Loop

Groqqy implements the ReAct pattern (Reasoning + Acting) for true multi-step problem solving:

User: "Find Python files and count their lines"
     ↓
┌────────────────┐
│ THINK (LLM)    │  "I need to search for .py files first"
└────────┬───────┘
         ↓
┌────────────────┐
│ ACT (Tool)     │  execute: search_files("*.py")
└────────┬───────┘
         ↓
┌────────────────┐
│ OBSERVE        │  "Found: app.py, test.py, utils.py"
└────────┬───────┘
         ↓
┌────────────────┐
│ THINK (LLM)    │  "Now read each file and count lines"
└────────┬───────┘
         ↓
┌────────────────┐
│ ACT (Tools)    │  execute: read_file("app.py"), read_file("test.py")...
└────────┬───────┘
         ↓
     [Response]

See docs/ARCHITECTURE.md for deep dive.

🛠️ Built-in Tools

• read_file(file_path) - Read file contents
• run_command(command) - Execute shell commands (secure with shlex)
• search_files(pattern, path) - Find files by glob pattern
• search_content(query, path) - Search text in files (ripgrep)

🌐 Platform Tools (v2.0+)

Execute tools on Groq's servers (e.g., web search):

from groqqy import Groqqy, ToolRegistry

registry = ToolRegistry()
registry.register_platform_tool("browser_search")

bot = Groqqy(model="llama-3.3-70b-versatile", tools=registry)

response, cost = bot.chat(
    "What are the latest AI developments this week?"
)
# Agent uses browser_search to get current web information

📝 Conversation Export

Export full conversations with tool calls and results:

# During a session
bot.chat("Calculate the weather in NYC")
bot.chat("What's 15% tip on $87.50?")

# Export to markdown
bot.save_conversation("session.md")

# Export to styled HTML
bot.save_conversation("session.html")

CLI auto-export:

groqqy --export my_session.html

Interactive export:

You: export markdown conversation.md
✅ Conversation exported to conversation.md

Exports include:

• All user messages
• All assistant responses
• All tool calls with JSON arguments
• All tool results
• Timestamps and metadata

Perfect for documentation, debugging, or sharing agent behavior.

🎯 Strategy Pattern

Automatic tool execution strategy selection:

• LocalToolStrategy: Execute tools in your environment
• PlatformToolStrategy: Execute on Groq's servers (browser_search, etc.)
• HybridToolStrategy: Mix local and platform tools intelligently

No configuration needed—strategies auto-detect based on tool types.

Examples

Check out the examples/ directory:

• basic_chat.py - Simple conversation
• custom_tools.py - Adding custom tools with decorator pattern
• tool_usage.py - Tool calling and chaining
• export_conversation.py - Exporting to markdown/HTML
• example_web_search.py - Using platform tools for web access
• reveal_mvp_demo.py - Self-discovery pattern with reveal-cli
• self_discovery_demo.py - Autonomous tool learning

Run any example:

python examples/basic_chat.py

Documentation

• docs/USER_GUIDE.md - Comprehensive usage guide
• docs/ARCHITECTURE.md - Deep dive into design and components
• docs/TEACHING_GUIDE.md - Using Groqqy to learn/teach agentic AI
• docs/guides/ - Feature-specific guides and tutorials
• CONTRIBUTING.md - How to contribute
• DEVELOPMENT.md - Developer setup and workflow

Architecture

Groqqy is built with clean, composable components:

groqqy/
├── bot.py              # Simple facade (Groqqy class)
├── agent.py            # Agentic loop (THINK/ACT/OBSERVE)
├── strategy.py         # Tool execution strategies
├── tool.py             # Tool registry system
├── tools.py            # Built-in tools
├── components/
│   ├── conversation.py # Message history
│   ├── executor.py     # Tool execution
│   ├── exporter.py     # Conversation export
│   └── tracker.py      # Cost tracking
└── providers/
    └── groq.py         # Groq API integration

Design Principles:

• All files <365 lines (most <200)
• Single responsibility per component
• Easy to read, understand, and modify
• Production-ready patterns (logging, error handling, cost tracking)

Teaching & Learning

Groqqy is designed as a teaching kernel for agentic AI. Unlike production frameworks (LangChain, LangGraph) with 50,000+ lines of code, Groqqy is:

• ✅ ~1,500 lines for complete agentic loop
• ✅ 88-line core algorithm - read and understand in 5 minutes
• ✅ Explicit patterns - THINK/ACT/OBSERVE labeled in code
• ✅ Production-ready - not toy code, real patterns
• ✅ Pedagogical - designed for learning then extending

Perfect for:

• Computer science courses on AI agents
• Self-learners exploring agentic patterns
• Developers understanding agents before using frameworks
• Workshops and tutorials on tool-calling LLMs

See docs/TEACHING_GUIDE.md for lesson plans and learning paths.

Cost Examples

Real costs from actual usage:

Task	Cost
Simple conversation	$0.000022
Search files	$0.000028
Run command	$0.000032
Multi-step task (3 tools)	~$0.000120
1,000 interactions	~$0.03-$0.12

Compare to GPT-4: ~$10-$100 for 1,000 interactions (300x more expensive)

Testing

# Run all tests
pytest

# Run specific test category
pytest tests/unit/
pytest tests/integration/
pytest tests/examples/

# Run with coverage
pytest --cov=groqqy

# Container testing (reproducible environment)
./container_test.sh

Configuration

Groqqy supports persistent configuration via ~/.groqqy/:

~/.groqqy/
├── boot.md              # System instructions loaded on startup
└── knowledge/           # Additional context files
    └── domain_info.md

CLI options:

groqqy                              # Interactive with boot.md
groqqy --prompt "What's 2+2?"       # Single-shot
groqqy --model llama-3.3-70b-versatile  # Custom model
groqqy --export chat.html           # Auto-export on exit
groqqy --no-boot                    # Skip boot.md

Models

Groqqy supports all Groq models:

# Fast and cheap (default)
bot = Groqqy(model="llama-3.1-8b-instant")

# Fastest (460+ tok/sec)
bot = Groqqy(model="llama-4-scout")

# Best quality
bot = Groqqy(model="llama-3.3-70b-versatile")

# Mixture of experts
bot = Groqqy(model="mixtral-8x7b-32768")

# Platform tools (required for browser_search)
bot = Groqqy(model="llama-3.3-70b-versatile")  # or openai/gpt-oss-20b

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Quick start for contributors:

# Clone and install
git clone https://github.com/scottsen/groqqy.git
cd groqqy
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black groqqy/ tests/

# Type check
mypy groqqy/

License

MIT License - see LICENSE file for details.

Related Projects

• Groq API Docs - Groq's LPU inference
• ReAct Paper - Reasoning + Acting framework (Yao et al., 2022)
• LangChain - Production agentic framework
• LangGraph - Graph-based agents

Support & Community

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Examples: Check examples/ directory

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Built with ❤️ using Groq's blazing-fast LPU inference

Perfect for: Learning agentic AI • Rapid prototyping • Cost-conscious automation • Teaching AI agents • Building proof-of-concepts