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lyzr-automata

Lyzr Automata is a low-code multi-agent automation framework used to build multiple agents and assign them specific tasks, supporting workflow automation. The framework divides its core concepts into foundational modules such as Models, Agents, Tools, Tasks, and Pipelines. Users can connect large language models such as OpenAI and Perplexity, create agents with specific roles, personas, and memory, use preset tools or custom tools to perform operations such as external API calls, and execute tasks in sequence through linear asynchronous pipelines. The framework is developed by LyzrCore and is an experimental project. Its official description states that it is developed quickly, and the initial version may be imperfect, but it will continuously improve stability and structure, and community contributions are welcome.

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About this tool

Purpose

Lyzr Automata is a low-code multi-agent automation framework designed to build workflow automation in a concise way. It allows the creation of multiple agents and binds a specific task to each agent. Agents and tasks can run independently, complete instructions, and enter a stable state. The framework focuses on the efficiency and effectiveness of workflows.

Main Capabilities

The framework consists of several foundational building blocks:

  • Models: The core unit for connecting different LLMs or other AI models to workflows. You can use the preset OpenAI and Perplexity model classes, or extend the base AIModel class to support your own models.
  • Agents: Targeted experts with set roles, personas, and memory, used to give direction and expertise to LLM models and improve results. Official examples include creating a LinkedIn content creator agent.
  • Tools: Used by agents to complete tasks, usually to connect external software components such as APIs or other functions. You can use preset tools, third-party tools such as Llama Hub, or customize tools through the base Tool class (you need to provide the function and input/output Pydantic models).
  • Tasks: The smallest functional unit/node, defining what you want the agent to complete, combining agents with tools. Official examples include defining tasks and tool tasks.
  • Pipelines: Run tasks in a directed, ordered way. Currently, linear asynchronous pipelines are supported, and asynchronous DAG pipelines are planned for future versions.

Usage

Installation: The package can be installed by running the corresponding installation command (the official text does not provide the specific installation command). Usage process: First create a model instance (such as an OpenAI or Perplexity model, optionally with parameters), then create an agent and give it a role, persona, and memory, then equip the agent with tools (preset or custom), then define tasks, and finally execute the tasks in sequence through a pipeline. The official provides two Colab examples, LinkedIn posting automation and blog automation, with links respectively:https://colab.research.google.com/drive/1lVJrdjHVZjbwZSqwJEU_etHC4GM3ihD0?usp=sharing and https://colab.research.google.com/drive/1e1Qa2pRjbngvX683pXTFLyuqLK11h967?usp=sharing#scrollTo=TmBixqI_BULu。

Applicable Scenarios

Suitable for workflow scenarios that require multi-agent collaboration and task automation, such as automatically generating LinkedIn posts, blog automation, etc., as well as processes that need to connect to external APIs or tools to complete tasks.

Limitations and Notes

The official description states that this framework is the product of rapid experimentation, and the initial version may have imperfections. The team is committed to improving stability and structure in subsequent versions, and encourages community contributions. Currently, pipelines only support linear asynchronous, and asynchronous DAG pipelines are still planned. Other details (such as specific installation commands, pricing methods, etc.) are not yet provided.

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