Skip to main content

A Python Automation Library for creating agents

Project description

Maticlib Logo

A high-performance Python Automation Library for creating intelligent AI agents.

License: MIT Downloads PyPI version

Maticlib is a developer-centric library designed to build complex, stateful AI workflows with ease. Whether you need a simple chat completion or a multi-node parallel execution graph, Maticlib provides the primitives to build it with a focus on type safety and performance.


Key Features

  • Core Agent Framework: Pure-Python graph workflow engine (MaticGraph) supporting both stateful and stateless execution.
  • Unified LLM Integration: Single interface for OpenAI (modern Responses API), Google Gemini, and Mistral AI.
  • Complex Orchestration: Built-in support for conditional routing, parallel node execution, and loop prevention.
  • Data Integrity: Deep Pydantic integration for standardized response models and type-safe workflow states.
  • Advanced Telemetry: Detailed token usage tracking, including modalities and reasoning tokens for newer models.

Documentation

For complete documentation, visit: https://arvohsoft.github.io/maticlib/


Installation

pip install maticlib

Quick Start

from maticlib.llm.openai import OpenAIClient

# Initialize client (uses OPENAI_API_KEY from environment)
client = OpenAIClient()

# Make a request
response = client.complete("Explain quantum computing in one sentence.")
print(client.get_text_response(response))

Roadmap

Core Library

  • Google Gemini integration
  • Mistral AI integration
  • OpenAI Responses API integration
  • Graph-based workflow engine
  • Parallel node execution
  • Automated documentation system
  • Anthropic Claude integration
  • Streaming support for all providers

Advanced Features

  • Unified tool/function calling interface
  • Multi-agent collaboration protocols
  • MCP (Model Context Protocol) support
  • Workflow persistence & checkpointing

Project Leadership

Maticlib is developed and maintained by Arvoh Software.


License

This project is licensed under the MIT License - see the LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

maticlib-0.1.5.tar.gz (35.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

maticlib-0.1.5-py3-none-any.whl (42.7 kB view details)

Uploaded Python 3

File details

Details for the file maticlib-0.1.5.tar.gz.

File metadata

  • Download URL: maticlib-0.1.5.tar.gz
  • Upload date:
  • Size: 35.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for maticlib-0.1.5.tar.gz
Algorithm Hash digest
SHA256 51fd847769ba43297a72ca815829ece187eed5a8cfa36a6f8cfb7c1d412ce96e
MD5 a31ceeee2c67fed795c28e2720a44b4e
BLAKE2b-256 764e3ff62682e4b82f23043a9a258cdc8f809606cade023c8ecec837c9f232db

See more details on using hashes here.

File details

Details for the file maticlib-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: maticlib-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 42.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for maticlib-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 9ee4a50e8924bdbba99f4e663f440c2780f949d2efef7d6ef340474af17e48d0
MD5 bdca938f85ff8bd44e52569d0b2d6e57
BLAKE2b-256 fa5c15107feffb14d41a3434dc4724f90274390502a27b1f078ff432a2e4209e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page