Magentic

Visit Tool

Magentic is an AI Agents & Automation tool that seamlessly integrates Large Language Models (LLMs) as Python functions. It allows developers to create functions that return structured output from an LLM using decorators.

Claim this tool

9Views

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is magentic?

Magentic is a Python library designed to seamlessly integrate Large Language Models (LLMs) into Python code, enabling developers to build complex agentic systems. It leverages `@prompt` and `@chatprompt` decorators to define functions that interact with LLMs, returning structured outputs based on Pydantic models and built-in Python types. Key features include streaming of structured outputs and function calls, LLM-assisted retries for adherence to complex schemas, and observability via OpenTelemetry. Magentic supports multiple LLM providers like OpenAI and Ollama, offering flexible configuration options. It also facilitates asynchronous operations and chaining of LLM calls for sophisticated workflows.

Best used for

Ideal for developers who need to integrate LLMs directly into their Python code, build complex agentic systems, and ensure structured and reliable LLM outputs. Especially valuable for creating applications that require advanced LLM interaction, function calling, and asynchronous processing.

Common actions

integrate LLMs

build AI agents

stream LLM output

define LLM functions

orchestrate LLM workflows

open-sourceautomated workflowcollaborationdeepfakelow-code/no-code"AI Agents"workflowsface swappinggithub copilot

Capabilities

Key features

Integrate LLMs as Python functions
Structured outputs with Pydantic
Stream outputs and function calls
LLM-assisted retries
OpenTelemetry observability
Support multiple LLM providers
Asyncio for concurrent queries

Target Audience

developer

Integrations

openaiollama

Pricing & Plans

Open Source

Free

FAQs

What LLM providers does Magentic support?

Magentic primarily supports OpenAI as its default backend. Additionally, it can integrate with Ollama via its OpenAI-compatible API, allowing users to leverage local models like Llama3.2. The framework is designed to be extensible for other OpenAI-compatible providers.

How does Magentic handle structured outputs from LLMs?

Magentic uses Pydantic models and built-in Python types to ensure structured outputs. Developers can define the expected return type for their LLM-powered functions, and Magentic will guide the LLM to generate output conforming to that schema, including streaming capabilities.

Can Magentic functions call other functions or LLMs?

Yes, Magentic supports both LLM-initiated function calls and chaining of LLM-powered functions. The `@prompt_chain` decorator automatically resolves `FunctionCall` objects, allowing the LLM to make multiple calls and use their results to formulate a final answer, enabling complex agentic behaviors.

Trending

Subcategories trending in AI Agents & Automation

Chatbots & Conversational AI General-Purpose Agents Workflow Agents Personal Assistants RAG & Document AI Voice Agents

Trending

Also listed in

This tool also appears in

Coding & Development › Code Assistants Coding & Development › Prompt Engineering

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce