Intellibricks: Build Intelligent Apps, Python-First

## The AI Development Pain Points, Solved.

Building AI applications can be complex. Intellibricks simplifies the process, addressing common frustrations developers face with common frameworks.

🤯

### Complexity Overload

Traditional AI frameworks often introduce unnecessary layers of abstraction. Intellibricks offers a streamlined, Python-first approach to reduce complexity and cognitive load.

🤔

### Unpredictable LLM Outputs

Achieving reliable and structured outputs from Language Models can be challenging. Intellibricks leverages Python's type system to ensure predictable and well-formatted responses, every time.

⚙️

### Boilerplate Blues

Repetitive setup and configuration can stifle creativity. Intellibricks eliminates boilerplate, allowing you to focus on building intelligent logic, not wrestling with infrastructure.

## Core Modules: Intelligent Building Blocks for Your AI Applications

Intellibricks is structured around three core modules, each designed for power, flexibility, and seamless integration, empowering you to build sophisticated AI applications with ease.

🧱

### LLMs Module: Speak Fluently with AI Models

Your gateway to Language Model Models (LLMs). Interact with various LLMs in a consistent and Pythonic manner. Synapses for effortless model switching, structured outputs for reliable data, and built-in observability for debugging.

- Synapses: Connect to Google Gemini, OpenAI, Groq, and more with one line of code.
- Structured Outputs: Define data models with Python classes using `msgspec.Struct`.
- Chain of Thought: Structured reasoning with `ChainOfThought` for enhanced observability.
- Langfuse Observability: Built-in integration for tracing and debugging.

    from intellibricks.llms import Synapse

    synapse = Synapse.of("google/genai/gemini-pro-experimental")
    completion = synapse.complete("Write a poem about Python.")
    print(completion.choices[0].message.contents)

Learn more about LLMs Module →
🤖

### Agents Module: Craft Autonomous Intelligent Entities

Empower yourself to build sophisticated, autonomous agents capable of performing complex tasks. Agents orchestrate LLM interactions and leverage tools to achieve specific goals, turning your intelligent logic into reality.

- Agent Class: Define tasks, instructions, metadata, and connect to Synapses.
- Tool Calling: Equip agents with tools for real-world interaction and function calling.
- Instant APIs: Turn agents into REST APIs with FastAPI/Litestar effortlessly.
- Context-Aware Agents: Seamless RAG integration for informed, intelligent agents.

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

    synapse = Synapse.of("openai/api/gpt-4o")
    agent = Agent(..., synapse=synapse)
    agent_response = agent.run("Knight discovers dragon egg.")
    print(f"Agent suggests: {agent_response.text}")

Learn more about Agents Module →

## Structured Outputs: Data, Defined by You.

Getting structured data from LLMs is critical for building robust applications. Intellibricks makes it Pythonically simple and blazingly fast. See how we compare:

Feature | Intellibricks | LangChain | LlamaIndex
--- | --- | --- | ---
AI responses | Uses modern python (e.g Default Generics) to predict AI structured responses | Responses are obscured by weak typing | Responses are obscured by weak typing
Syntax Simplicity | Desired Outputs are passed in Agent's constructor. | `.with_structured_output(...) # (extra method)` | `.as_structured_llm(...) # (extra method)`
Performance | 🚀 Blazing Fast enc/dec with `msgspec` | Slow Pydantic | Slow Pydantic
Comprehensive Response | ✅ `AgentResponse` with full completion details | Pydantic object only | ChatResponse object with weak typing (see in README.md)

Intellibricks offers a more streamlined, efficient, and Python-centric approach to structured outputs, ensuring you get not just the data, but the full context of the LLM's response.

## 🏆 The Intellibricks Advantage: Python-First Framework

We believe in empowering developers with tools that feel natural, intuitive, and deeply Pythonic. Here’s why Intellibricks is the intelligent choice for your next AI application:

🐍

#### Idiomatic Python at its Core

Built with idiomatic Python, Intellibricks leverages modern Python features for a development experience that feels fluid and natural. No steep learning curves, no framework jargon – just clean, modern Python.

Benefit:

Write AI applications using the language you love, without fighting against framework conventions.

✨

#### Unmatched Simplicity and Clarity

Designed for intuitiveness and ease of use, Intellibricks dramatically reduces complexity and boilerplate. Focus on your application's intelligence, not intricate framework mechanics. Develop with clarity and confidence.

Benefit:

Get up and running quickly, iterate faster, and maintain your codebase with ease.

🧱

#### Structured Outputs as a Core Strength

Harness the power of structured data effortlessly. Define your data models with Python classes and let Intellibricks ensure perfectly formatted responses from LLMs, leveraging `msgspec` for blazing-fast performance.

Benefit:

Eliminate messy string parsing and work directly with clean, validated data structures.

🧠

#### Focus on Building Intelligence, Not Infrastructure

Intellibricks handles the plumbing, so you can concentrate on what truly matters: building intelligent logic. Spend less time on framework intricacies and more time crafting smart, innovative applications that solve real-world problems.

Benefit:

Maximize your development efficiency and bring intelligent solutions to life faster.

## Ready to Build Intelligent Applications?

Join the Intellibricks revolution and experience the future of AI development, the Python way.

`pip install intellibricks`
Get Started on PyPI → View on GitHub →