maticlib 0.1.1

A Python Automation Library for creating agents

Maticlib

A Python automation library for creating intelligent agents with easy-to-use API clients for multiple LLM providers including Google Gemini and Mistral AI.

Features

• 🤖 Simple and intuitive API for building AI agents
• 🔄 Synchronous and asynchronous request support
• 🛠️ Multiple LLM provider support (Google Gemini, Mistral AI)
• 📝 Built-in error handling and verbose logging
• 🚀 Lightweight with minimal dependencies
• 🔑 Environment variable support for API keys
• 💬 Multi-turn conversation support

Installation

From PyPI (Production)

pip install maticlib

From TestPyPI (Development)

pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ maticlib

From Source

git clone https://github.com/arvohsoft/maticlib.git
cd maticlib
pip install -e .

Quick Start

Google Gemini

from maticlib.llm.google_genai import GoogleGenAIClient

# Initialize with API key
client = GoogleGenAIClient(api_key="YOUR_GOOGLE_API_KEY")

# Or use environment variable GOOGLE_API_KEY
client = GoogleGenAIClient()

# Make a request
response = client.complete("Hello! Tell me about Python")
print(response.json())

Mistral AI

from maticlib.llm.mistral import MistralClient

# Initialize with API key
client = MistralClient(api_key="YOUR_MISTRAL_API_KEY")

# Or use environment variable MISTRAL_API_KEY
client = MistralClient()

# Make a request
response = client.complete("What is the best French cheese?")
print(response.json())

Generic Client

from maticlib.core.client import BaseClientModelURL

# For custom API endpoints
client = BaseClientModelURL(
    inference_url="https://api.example.com/v1/chat",
    header={"Authorization": "Bearer YOUR_API_KEY"},
    model="model-name",
    payload={"messages": []},
    verbose=True
)

response = client.complete("Your prompt here")

Usage Examples

Google Gemini with Custom Configuration

from maticlib.llm.google_genai import GoogleGenAIClient

client = GoogleGenAIClient(
    model="gemini-2.5-flash",  # or "gemini-pro", etc.
    api_key="YOUR_API_KEY",
    thinking_budget=0,
    verbose=True
)

response = client.complete("Explain quantum computing")
print(response.json())

Mistral AI with Different Models

from maticlib.llm.mistral import MistralClient

# Use different Mistral models
client = MistralClient(
    model="mistral-large-latest",  # or "mistral-medium-latest", "mistral-small-latest"
    api_key="YOUR_API_KEY"
)

response = client.complete("Write a short poem about coding")
print(response.json())

Multi-turn Conversations

from maticlib.llm.mistral import MistralClient

client = MistralClient(api_key="YOUR_API_KEY")

# Pass conversation history as list of messages
messages = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hi! How can I help you?"},
    {"role": "user", "content": "What's the weather like?"}
]

response = client.complete(messages)
print(response.json())

Asynchronous Usage

import asyncio
from maticlib.llm.google_genai import GoogleGenAIClient

async def main():
    client = GoogleGenAIClient(api_key="YOUR_API_KEY")
    response = await client.async_complete("Tell me a joke")
    print(response.json())

asyncio.run(main())

API Reference

GoogleGenAIClient

Client for Google Gemini API.

Parameters

• model (str): Model name (default: "gemini-2.5-flash")
• api_key (str): Google API key (or use GOOGLE_API_KEY env var)
• thinking_budget (int): Budget for model thinking (default: 0)
• verbose (bool): Enable verbose logging (default: True)

Methods

complete(prompt: str) -> httpx.Response

Make a synchronous completion request.

async_complete(prompt: str) -> httpx.Response

Make an asynchronous completion request.

MistralClient

Client for Mistral AI API.

Parameters

• model (str): Model name (default: "mistral-large-latest")
• api_key (str): Mistral API key (or use MISTRAL_API_KEY env var)
• verbose (bool): Enable verbose logging (default: True)

Methods

complete(prompt: str | list) -> httpx.Response

Make a synchronous completion request. Accepts string or message list.

async_complete(prompt: str | list) -> httpx.Response

Make an asynchronous completion request. Accepts string or message list.

BaseClientModelURL

Generic client for custom API endpoints.

Parameters

• inference_url (str): API endpoint URL
• header (dict): HTTP headers for authentication
• model (str): Model identifier
• payload (dict): Base payload structure
• verbose (bool): Enable verbose logging (default: True)

Environment Variables

Set environment variables for automatic API key loading:

export GOOGLE_API_KEY="your-google-api-key"
export MISTRAL_API_KEY="your-mistral-api-key"

Then use clients without explicitly passing keys:

from maticlib.llm.google_genai import GoogleGenAIClient
from maticlib.llm.mistral import MistralClient

google_client = GoogleGenAIClient()  # Uses GOOGLE_API_KEY
mistral_client = MistralClient()      # Uses MISTRAL_API_KEY

Error Handling

from maticlib.llm.mistral import MistralClient
from maticlib.exceptions import ClientError

try:
    client = MistralClient(api_key="YOUR_API_KEY")
    response = client.complete("Your prompt")
    print(response.json())
except ClientError as e:
    print(f"Client error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Development

Setting Up Development Environment

# Clone the repository
git clone https://github.com/arvohsoft/maticlib.git
cd maticlib

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate  # Windows

# Install dependencies
pip install -e ".[dev]"

Running Tests

pytest

Code Formatting

black maticlib/

Type Checking

mypy maticlib/

Requirements

• Python >= 3.8
• httpx >= 0.24.0

Supported LLM Providers

• Google Gemini - All Gemini models (gemini-2.5-flash, gemini-pro, etc.)
• Mistral AI - All Mistral models (mistral-large-latest, mistral-medium-latest, etc.)
• Custom - Any OpenAI-compatible API endpoint

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Support

For support, email [e-mail] or open an issue on GitHub.

Roadmap

LLM Provider Support

• Google Gemini integration
• Mistral AI integration
• OpenAI integration
• Anthropic Claude integration
• Cohere integration
• AWS Bedrock integration
• Ollama integration (local models)

Tool Integration & Function Calling

• Unified tool/function calling interface across all LLM providers
• Tool schema validation and type checking
• Built-in tool registry for common operations (web search, file operations, calculations)
• Custom tool creation framework with decorators
• Automatic tool result formatting and error handling
• Tool execution tracking and logging

Output Standardization

• Unified response format across all LLM providers
• Pydantic-based structured output support
• JSON schema validation for all responses
• Automatic type conversion and serialization
• Response normalization layer
• Provider-agnostic result objects

Orchestration & Workflow

• Graph-based workflow engine for complex agent interactions
• Visual workflow builder and debugger
• Conditional branching and parallel execution
• State management across workflow steps
• Loop detection and prevention
• Workflow templates for common patterns
• Event-driven execution model

Agent Framework

• Standalone agent creation with custom roles and goals
• Multi-agent collaboration system
• Agent communication protocols
• Task delegation and assignment
• Shared memory and knowledge base between agents
• Agent hierarchies and teams
• Dynamic agent creation and termination
• Agent performance monitoring

Model Context Protocol (MCP)

• MCP client implementation for consuming external tools
• MCP server implementation for exposing tools
• Resource and prompt management via MCP
• Support for MCP transport layers (stdio, HTTP)
• Built-in MCP tool registry
• MCP session management

Prompt Management

• Centralized prompt hub with curated templates
• Prompt versioning and A/B testing
• Prompt optimization suggestions
• Domain-specific prompt collections (coding, writing, analysis)
• Prompt chaining and composition
• Variable interpolation and templating
• Multilingual prompt support

Terminal User Interface (TUI)

• Rich terminal output with color-coded messages
• Real-time tool execution visualization
• Progress bars for long-running operations
• Interactive agent conversation display
• Workflow step visualization
• Error highlighting and debugging info
• Configurable verbosity levels
• Export TUI sessions to logs

Telemetry & Observability

• OpenTelemetry integration
• Request/response tracing
• Cost tracking per provider
• Token usage analytics
• Performance metrics (latency, throughput)
• Error rate monitoring
• Custom metric collection
• Integration with observability platforms (Prometheus, Grafana)

Core Improvements

• Streaming response support for all providers
• Automatic retry mechanisms with exponential backoff
• Circuit breaker pattern for provider failures
• Request rate limiting and queuing
• Response caching layer
• Comprehensive test coverage (>90%)
• Enhanced error handling with detailed error types
• Async-first architecture throughout

Developer Experience

• Interactive CLI for quick testing
• VSCode extension for code completion
• Comprehensive API documentation with examples
• Tutorial notebooks and video guides
• Migration guides from other frameworks
• Community-contributed recipes
• Performance benchmarking tools

Security & Compliance

• API key rotation and management
• Request encryption and signing
• PII detection and filtering
• Audit logging
• Role-based access control
• Compliance reporting (GDPR, SOC2)

Advanced Features

• Fine-tuning management interface
• Model evaluation and benchmarking suite
• Prompt injection detection
• Content moderation and safety filters
• Multi-modal support (images, audio, video)
• Vector database integration
• RAG (Retrieval-Augmented Generation) framework
• Agent memory persistence (short-term, long-term)

Acknowledgments

• Built with httpx for modern async HTTP requests
• Inspired by the need for simple, flexible AI agent creation
• Supports Google Gemini and Mistral AI APIs

Links

• Homepage: https://github.com/arvohsoft/maticlib
• PyPI: https://pypi.org/project/maticlib/
• Issues: https://github.com/arvohsoft/maticlib/issues
• Changelog: CHANGELOG.md

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About

Maticlib is developed and maintained by Arvoh Software.

Main Contributor: Anubroto Ghose
Organization: Arvoh Software
Email: [e-mail]

Made for developers building intelligent AI agents

Contributors

We welcome contributions! See CONTRIBUTING.md for details on how to get involved.