tiny-agent-os 0.72.12

A streamlined framework for building powerful LLM-powered agents that can solve complex tasks through tool execution, orchestration, and dynamic capability creation.

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tinyAgent 🤖

Why tinyAgent?

Turn any Python function into an AI‑powered agent in three lines:

from tinyagent.decorators import tool
from tinyagent.agent import tiny_agent

@tool                  # 1️⃣  function → tool
def add(a: int, b: int) -> int:
    return a + b

agent = tiny_agent(tools=[add])             # 2️⃣  tool → agent
print(agent.run("add 40 and 2"))           # 3️⃣  natural‑language call
# → 42

• Zero boilerplate – just a decorator.
• Built‑in LLM orchestration – validation, JSON I/O, retry, fallback.
• ReAct Pattern Support – Advanced reasoning + acting pattern for complex multi-step tasks.
• Scales as you grow – add more tools or plug into tiny_chain without rewrites.

Made by (x) @tunahorse21 | A product of alchemiststudios.ai

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Heads Up

tinyAgent is in BETA until V1. It's working but still evolving! I can't guarantee it's 100% bug-free, but I'm actively improving it whenever I can between my day job and business.
Found something that could be better? Show off your skills and open an issue with a fix: I'd genuinely appreciate it!

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Overview

tinyAgent is a streamlined framework for building powerful, LLM-powered agents that solve complex tasks through tool execution, orchestration, and dynamic capability creation. Convert any Python function into a useful tool and then into an agent with minimal configuration, unlocking a world of scalable, modular possibilities.

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Installation & Setup

1. Install the Package

# Basic installation
pip install tiny_agent_os

# With observability features (recommended)
pip install "tiny_agent_os[traceboard]"

# With all features (RAG + observability)
pip install "tiny_agent_os[rag,traceboard]"

2. Get the Configuration Files

After installation, you'll need two configuration files:

# Create a basic config.yml
python -m tinyagent.config init

# Or download the example config directly
wget https://raw.githubusercontent.com/alchemiststudiosDOTai/tinyAgent/v0.65/config.yml

Create a .env file with your API keys:

# Download the example .env file
wget https://raw.githubusercontent.com/alchemiststudiosDOTai/tinyAgent/v0.65/.envexample -O .env

# Edit with your API keys
nano .env  # or use any text editor

3. Quick Start Example

from tinyagent.decorators import tool
from tinyagent.agent import tiny_agent
from tinyagent.observability.tracer import configure_tracing  # For tracing support

# Define a tool
@tool
def add(a: int, b: int) -> int:
    return a + b

# Enable tracing (optional)
configure_tracing()  # This reads your config.yml

# Create an agent (with tracing enabled)
agent = tiny_agent(tools=[add], trace_this_agent=True)

# Run it!
result = agent.run("add 40 and 2")
print(result)  # → 42

4. ReAct Pattern Example (NEW!)

For complex multi-step reasoning tasks, use the ReAct agent. The framework automatically tells the LLM about available tools, so you can use any function name:

from tinyagent.react.react_agent import ReActAgent
from tinyagent.decorators import tool
from tinyagent.agent import get_llm

# Define tools - any function name works!
@tool
def calculate(expression: str) -> float:
    """Evaluate a mathematical expression."""
    return eval(expression)

@tool
def add_numbers(a: float, b: float) -> float:
    """Add two numbers together."""
    return a + b

# Create ReAct agent
agent = ReActAgent()
agent.register_tool(calculate._tool)
agent.register_tool(add_numbers._tool)

# Run with reasoning steps - framework automatically handles tool discovery
result = agent.run_react(
    query="If I have 15 apples and give away 40%, how many do I have left?",
    llm_callable=get_llm(),
    max_steps=5
)
print(result)  # → "You have 9 apples left."

Key Features:

• ✅ Automatic tool discovery - Framework tells LLM about available tools
• ✅ No "Unknown tool" errors - LLM uses exact tool names from registration
• ✅ Zero configuration - Just register tools and run
• ✅ Multi-step reasoning - Handles complex queries requiring multiple tool calls

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Post-Installation Configuration

After installing (either via pip or from source), remember to configure your environment and .env files with relevant API keys from https://openrouter.ai

Both the config.yml and env work out of the box with a openrouter API, you can use any openai API, and the config has an example of a local LLM. The /documentation folder has more details and is being updated.

Features

• Modular Design: Easily convert any function into a tool.
• Flexible Agent Options: Use the simple orchestrator or advanced AgentFactory.
• ReAct Pattern: Built-in support for Reasoning + Acting pattern for complex multi-step reasoning tasks.
• Robust Error Handling: Improved debugging with custom exceptions and JSON parsing.
• Structured Output: Enforce JSON formats for consistent outputs.
• Comprehensive Observability: Built-in OpenTelemetry tracing with multiple exporters (console, OTLP, SQLite) and a web-based trace viewer.

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Acknowledgments & Inspirations

• my wife
• HuggingFace SmoLAgents
• Aider-AI
• And many other open-source contributors!

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Learn More

• Functions as Tools
• ReAct Pattern Guide
• tinyChain Overview (Note: tinyChain will be sunset soon in favor of ReAct pattern due to better performance and stability. Existing code will continue to work but won't receive updates.)
• RAG
• Observability

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Contact

For questions, suggestions, or business inquiries:

• Email: [e-mail]
• X: @tunahorse21
• Website: alchemiststudios.ai

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License

Business Source License 1.1 (BSL) This project is licensed under the Business Source License 1.1. It is free for individuals and small businesses (with annual revenues under $1M). For commercial use by larger businesses, an enterprise license is required. For licensing or usage inquiries, please contact: [e-mail]

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