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lyzr-automata

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Multi-Agent Platforms & Frameworks
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Basic Information

Lyzr Automata is a low-code multi-agent automation framework designed for developers to build and orchestrate autonomous agents for process automation. It provides fundamental building blocks—Models, Agents, Tools, Tasks, and Pipelines—so teams can wire language models and external functions into directed workflows. The framework supports prebuilt model adapters for OpenAI and Perplexity and lets users extend a base AIModel class to add other models. Agents are defined with role, persona and memory to guide model behavior. Tools connect agents to external APIs or functions and can be created with Pydantic input/output models. Pipelines run tasks in ordered flows, currently supporting linear sync and async pipelines. The project is described as experimental and actively accepting contributions and improvements. Installation is available via pip install lyzr-automata.

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App URL
https://github.com/LyzrCore/lyzr-automata
Github Repository
https://github.com/LyzrCore/lyzr-automata/blob/main/README.md

App Details

Features
Modular building blocks: clear separation of Models, Agents, Tools, Tasks and Pipelines to simplify composition of agent workflows. Model adapters: prebuilt classes for OpenAI and Perplexity with configurable parameters and an extendable AIModel base. Agent abstraction: role, persona and memory to give directed expertise to LLMs. Tool system: prebuilt connectors (for example a LinkedIn post tool) and a Tool base class that accepts a Python function plus Pydantic request/response models for safe typing. Task abstraction: combines an agent, a model and optionally a tool to produce typed outputs. Pipelines: linear sync and async pipeline implementations with task sequencing and completion messaging. Examples and Colab demos for LinkedIn post automation and blog automation are provided. Community channels and contact info are included.
Use Cases
The framework helps developers rapidly build repeatable, agent-driven automations without wiring low-level orchestration. By providing model adapters and agent/persona constructs it reduces prompt engineering overhead and lets teams focus on task logic. The Tool and Pydantic input/output pattern promotes safer integrations with external APIs and deterministic function calls. Tasks encapsulate single units of work and pipelines orchestrate those tasks into end-to-end flows so users can implement flows like content generation then publication. Prebuilt tools and examples accelerate common use cases such as LinkedIn posting or blog automation. The package is installable with pip and is positioned as a lightweight, low-code option for prototyping and iterating multi-agent automations while the project matures through community contributions.

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