tiny-agent-os 0.72.1

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 🤖

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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

Via pip (Recommended)

pip install tiny_agent_os

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Post-Installation Configuration for Pip Users

After installing via pip, you'll need to provide your own configuration files. For convenience, you can download the defaults directly:

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Download the Configuration File (config.yml)

Using wget:

wget https://raw.githubusercontent.com/alchemiststudiosDOTai/tinyAgent/v0.65/config.yml

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Download the Environment File (.env)

Download the example environment file and rename it to .env:

Using wget:

wget https://raw.githubusercontent.com/alchemiststudiosDOTai/tinyAgent/v0.65/.envexample -O .env

Note: Be sure to edit the .env file with your actual API keys and any other required variables.

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Cloning for Development

git clone https://github.com/alchemiststudiosDOTai/tinyAgent.git
cd tinyAgent

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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.

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Tools and the @tool Decorator

In tinyAgent, any Python function can be transformed into a usable "tool" by simply decorating it with @tool. This makes it discoverable by your agents, allowing them to execute that function in response to natural-language queries.

Example

from tinyagent.decorators import tool

@tool
def greet_person(name: str) -> str:
    """Return a friendly greeting."""
    return f"Hello, {name}!"

That's it! Once decorated, greet_person can be included in an agent's list of tools, letting your LLM-driven agent call it as needed.

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Philosophy

tinyAgent is built on two core ideas:

1. Functions as Agents

Any Python function can be turned into a tool—and then seamlessly integrated into an agent. This approach makes extending and innovating simple.

flowchart LR
    A["Python Function"] --> B["Tool"]
    B --> C["Agent"]
    C --> D["Result"]

#!/usr/bin/env python3
"""
Example: Functions as Agents
"""
from tinyagent.decorators import tool
from tinyagent.factory.agent_factory import AgentFactory

@tool
def calculate_sum(a: int, b: int) -> int:
    """Calculate the sum of two integers."""
    return a + b

def main():
    agent = AgentFactory.get_instance().create_agent(tools=[calculate_sum])
    query = "calculate the sum of 5 and 3"
    print(f"Query: '{query}'")
    result = agent.run(query, expected_type=int)
    print(f"Result: {result}")

if __name__ == "__main__":
    main()

2. tiny_chain Orchestration

Complex tasks are handled through an intelligent chain of tools that automatically:

• Selects appropriate tools
• Maintains context between steps
• Processes results in sequence

flowchart LR
    A["Task"] --> B["tiny_chain"]
    B --> C["Search Tool"]
    B --> D["Browser Tool"]
    B --> E["Summarize Tool"]
    C --> F["Context"]
    D --> F
    E --> F
    F --> G["Result"]

    style B fill:#f9f,stroke:#333,stroke-width:2px
    style F fill:#bbf,stroke:#333,stroke-width:2px

from tinyagent.factory.tiny_chain import tiny_chain
from tinyagent.tools.duckduckgo_search import get_search_tool
from tinyagent.tools.custom_text_browser import get_browser_tool
from tinyagent.decorators import tool

@tool(name="summarize")
def summarize_text(text: str) -> str:
    """Summarize the provided text."""
    return llm_summarize(text)  # Your LLM summarization logic

# Create chain with tools
chain = tiny_chain.get_instance(tools=[
    get_search_tool(),      # Search the web
    get_browser_tool(),     # Visit and extract content
    summarize_text._tool    # Summarize findings
])

# Execute complex task
task_id = chain.submit_task(
    "research latest AI developments and summarize key points"
)

# Get results
status = chain.get_task_status(task_id)
if status.result:
    for step in status.result['steps']:
        print(f"Step {step['tool']}: {step['result']}")

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Features

• Modular Design: Easily convert any function into a tool.
• Flexible Agent Options: Use the simple orchestrator or advanced AgentFactory.
• Robust Error Handling: Improved debugging with custom exceptions.
• Structured Output: Enforce JSON formats for consistent outputs.

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Acknowledgments & Inspirations

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

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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]