Intellibricks: The Building Blocks for Intelligent Applications 🧠🧱¶

The Python-First Framework for Agentic & LLM-Powered Applications

Are you ready to build truly intelligent applications with the ease and clarity of Python?

Welcome to IntelliBricks!

IntelliBricks is more than just another LLM framework. It’s a developer-centric toolkit, crafted from the ground up to empower you to create sophisticated AI applications with unprecedented simplicity. We believe that building with AI should feel as intuitive and natural as writing Python itself.

IntelliBricks helps you overcome the common challenges of AI development:

Complexity Overload: Simplify development with a streamlined, Python-first approach, reducing layers of abstraction.
Unpredictable LLM Interactions: Achieve reliable and structured outputs from Language Models using Python’s type system.
Boilerplate Blues: Eliminate repetitive setup and focus on building intelligence, not infrastructure.

Get Started Now!¶

pip install intellibricks

# Example Synapse Usage - Get a quick taste!
from intellibricks.llms import Synapse
synapse = Synapse.of("openai/api/gpt-3.5-turbo") # Or any free model
response = synapse.complete("Say hello in a funny way!")
print(f"IntelliBricks says: {response.text}")

Core Modules: Your Intelligent Toolkit¶

IntelliBricks is built around three core modules, each designed to be powerful individually and seamlessly integrated for building truly intelligent applications:

🧱 LLMs Module <llms>: Speak Fluently with AI Models¶

The intellibricks.llms module is your gateway to Language Model Models (LLMs). It provides the tools to interact with various LLMs in a consistent and Pythonic manner.

✅ Synapses: Smart adapters for connecting to different LLM providers (Google Gemini, OpenAI, Groq, and more). Switch models effortlessly!
✅ Structured Outputs: Define your data models in pure Python and get perfectly formatted responses from LLMs. Say goodbye to messy string parsing!
✅ Chain of Thought: Leverage structured reasoning with the built-in ChainOfThought class for enhanced observability and debugging.
✅ Observability: Seamless integration with Langfuse for tracing, monitoring, and debugging your LLM interactions.

Example: Basic Synapse Usage¶

from intellibricks.llms import Synapse

synapse = Synapse.of("google/genai/gemini-pro-experimental")
response = synapse.complete("Tell me a joke.")
print(response.text)

🤖 Agents Module <agents>: Craft Autonomous Intelligent Entities¶

The intellibricks.agents module empowers you to build sophisticated, autonomous agents capable of performing complex tasks. Agents orchestrate LLM interactions and leverage tools to achieve specific goals.

✅ Agent Class: The central building block for creating intelligent cores. Define tasks, instructions, metadata, and connect to Synapses.
✅ Tool Calling: Equip your agents with tools to interact with external systems, access data, and perform real-world actions.
✅ Effortless API Generation: Instantly turn your agents into production-ready REST APIs using FastAPI or Litestar with minimal code.

Example: Creating a Simple Agent¶

from intellibricks.agents import Agent
from intellibricks.llms import Synapse

synapse = Synapse.of("openai/api/gpt-4o")
agent = Agent(
    task="Joke Teller",
    instructions=["You are a funny joke teller."],
    synapse=synapse,
    metadata={"name": "JokeBot", "description": "Agent that tells jokes."},
)
response = agent.run("Tell me a joke about Python.")
print(response.text)

🗂️ Files Module <files>: Intelligent File Handling¶

The intellibricks.files module provides a robust way to handle and process files within your AI applications. Parse, extract, and understand content from various file types with ease.

✅ RawFile Abstraction: Represent files as RawFile objects, encapsulating content, name, and extension for easy manipulation.
✅ Parsed Files: Foundation for extracting structured information (text, images, tables) from files, making file data accessible to agents.
✅ File Parsing Infrastructure: Laying the groundwork for integrating file parsers for diverse file formats in future releases.

Example: Working with RawFile¶

from intellibricks.files import RawFile

raw_file = RawFile.from_file_path("document.pdf") # Or any file
print(f"File Name: {raw_file.name}")
print(f"File Extension: {raw_file.extension}")

🏆 Why Choose IntelliBricks? The Intelligent Choice¶

IntelliBricks stands out from other frameworks by prioritizing Python as a First-Class Citizen.

🐍 Python First: Built with idiomatic Python, leveraging modern features for a truly Pythonic development experience.
✨ Unmatched Simplicity & Clarity: Designed to be intuitive and easy to use, reducing complexity and boilerplate.
🧱 Structured Outputs Out-of-the-Box: Core strength in getting structured data from LLMs with blazingly fast and efficient definitions.
🧠 Focus on Intelligence: Concentrate on building intelligent logic, not framework intricacies. IntelliBricks handles the plumbing.

🚀 Join the IntelliBricks Revolution!¶

Ready to build truly intelligent applications, effortlessly?

Get Started: pip install intellibricks
Explore Examples: Dive into the Quickstart guide.
Contribute: IntelliBricks is community-driven! See our contribution guidelines to get involved.
Connect: Reach out with questions, feedback, and ideas!

Let’s build the future of intelligent applications, together!