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low-code multi-agent automation framework
| Date | Stars |
|---|---|
| 2026-07-31 | 276 |
| 2026-08-06 | 276 |
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 [](https://discord.gg/yQn8SScy) [](https://youtu.be/6U42TgaR6RA?feature=shared)
# Lyzr Automata
## 1. Introduction
### A low-code multi-agent automation framework.
Note : Framework was developed swiftly as an experiment, leading to initial imperfections. We're committed to enhancing its stability and structure in upcoming releases, ensuring a high-quality. We encourage contributions from everyone, aiming to advance the project further together.
### [](https://github.com/LyzrCore/lyzr-automata#lyzr-automata---autonomous-multi-agent-framework-for-process-automation)Lyzr Automata - Autonomous Multi-Agent Framework for Process Automation
Lyzr Automata is a sophisticated multi-agent automation framework designed to keep things simple, with a focus on workflow efficiency and effectiveness. It enables the creation of multiple agents that are coupled with specific tasks. The agents and tasks can run independently and complete the provided instructions, thus entering a stable state.
## 2. Installation
You can install our package simply by running
pip install lyzr-automata
## 3. Understanding fundamentals
To make things very simple, we have split our framework into fundamental building blocks.
1. Models
2. Agents
3. Tools
4. Tasks
5. Pipelines
### 1. Models
Models are the core unit of the framework, which helps you connect different LLM or other AI Models to workflows.
Note: You can use our prebuilt model classes for **OpenAI and Perplexity** or extend our base **AIModel** class to provide support for your own models.
1.1 *Let's look at an example to create a **OpenAI Model** with parameters*
```python
from lyzr_automata.ai_models.openai import OpenAIModel
open_ai_text_completion_model = OpenAIModel(
api_key="YOUR_OPEN_AI_KEY",
parameters={
"model": "gpt-4-turbo-preview",
"temperature": 0.2,
"max_tokens": 1500,
},
)
```
1.2 *Let's look at an example to create a **Perplexity Model** with parameters*
```python
from lyzr_automata.ai_models.perplexity import PerplexityModel
perplexity_model_text = PerplexityModel(
api_key="YOUR_KEY_HERE",
parameters={
"model": "pplx-70b-online",
},
)
```
### 2. Agents
Agents are directed specialists which have a set role, persona and memory, they are used to set direction and expertise for LLM models to increase its effectiveness.
2.1 *Let's look at an example to create a simple linkedin content creator agent.*
```python
from lyzr_automata import Agent
linkedin_content_creator_agent = Agent(
role="linkedin content creator",
prompt_persona="you are an expert linkedin content creator who holds a Phd in molecular biology and are good at creating engaging linkedin posts."
)
```
### 3. Tools
Tools are used by Agents to complete tasks. In general sense tools help agents to connect with external software components like API's or other functions.
*Note: You can use our pre-built tools or tools from other providers like **Llama Hub**. Optionally you can also use our base **Tool** class to create your own custom tool by providing the function, input and output Pydantic models.*
*3.1 Let's look at an example to use prebuilt linkedin post tool*
```python
from lyzr_automata.tools.prebuilt_tools import linkedin_image_text_post_tool
linkedin_post_tool = linkedin_image_text_post_tool(
owner="urn:li:person:<YOUR_ID_HERE>",
token="YOUR_TOKEN_HERE",
)
```
*3.2 Let's look at an example to use our tool class to create your ownExcerpt of 7,079 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:579e43bdf3d90909, llm:Repository description: "low-code multi-agent automation framework" (topics none).
matched fp:579e43bdf3d90909, llm:Repository description: "low-code multi-agent automation framework" (topics none).