Intellibricks is the Python-first toolkit designed to streamline AI application development. Focus on your logic, not framework complexity.
Building AI applications can be complex. Intellibricks simplifies the process, addressing common frustrations developers face with common frameworks.
Traditional AI frameworks often introduce unnecessary layers of abstraction. Intellibricks offers a streamlined, Python-first approach to reduce complexity and cognitive load.
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.
Repetitive setup and configuration can stifle creativity. Intellibricks eliminates boilerplate, allowing you to focus on building intelligent logic, not wrestling with infrastructure.
Intellibricks is structured around three core modules, each designed for power, flexibility, and seamless integration, empowering you to build sophisticated AI applications with ease.
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.
msgspec.Struct.
ChainOfThought for
enhanced observability.
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)
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.
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}")
Provides a robust way to handle and process files within your AI applications. Parse, extract, and understand content from various file types with ease, making file data seamlessly accessible to your agents and workflows.
RawFile objects for
easy manipulation.
from intellibricks.files import RawFile, PdfFileParser
raw_file = RawFile.from_file_path("document.pdf")
print(f"File Name: {raw_file.name}")
print(f"File Extension: {raw_file.extension}")
parser = PdfFileParser()
parsed_file = parser.parse(raw_file) # ParsedFile
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.
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:
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.
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.
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.
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.
Join the Intellibricks revolution and experience the future of AI development, the Python way.
pip install intellibricks