Aurora

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Aurora is an open-source agent orchestration framework that empowers developers to build autonomous generative AI agents. It offers planner and executor modules to decompose goals into subtasks, integrate external tools, manage memory, and handle dynamic inputs. By leveraging LLM-driven planning and execution loops, Aurora simplifies creating agents that can reason, plan, and interact with APIs or data sources in real time.
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May 13 2025
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Aurora

Aurora

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0
Aurora
Aurora is an open-source agent orchestration framework that empowers developers to build autonomous generative AI agents. It offers planner and executor modules to decompose goals into subtasks, integrate external tools, manage memory, and handle dynamic inputs. By leveraging LLM-driven planning and execution loops, Aurora simplifies creating agents that can reason, plan, and interact with APIs or data sources in real time.
Added on:
Social & Email:
Platform:
May 13 2025
--
Featured

What is Aurora?

Aurora provides a modular architecture for constructing generative AI agents that can autonomously tackle complex tasks through iterative planning and execution. It consists of a Planner component that breaks down high-level objectives into actionable steps, an Executor that invokes these steps using large language models, and a Tool integration layer for connecting APIs, databases, or custom functions. Aurora also includes memory management for context retention and dynamic re-planning capabilities to adjust to new information. With customizable prompts and plug-and-play modules, developers can rapidly prototype AI agents for tasks like content generation, research, customer support, or process automation, while maintaining full control over the agent’s workflows and decision logic.

Who will use Aurora?

  • AI Developers
  • Machine Learning Engineers
  • Technical Product Managers
  • Research Scientists
  • Open-source Enthusiasts

How to use the Aurora?

  • Step1: Install Aurora via pip with pip install git+https://github.com/kyegomez/Aurora.git
  • Step2: Set environment variables for your LLM API key (e.g., OPENAI_API_KEY)
  • Step3: Define custom tool interfaces and register them in the tool integration layer
  • Step4: Initialize the Planner and Executor modules with your configurations
  • Step5: Configure the agent pipeline by combining Planner, Executor, and Memory modules
  • Step6: Run the agent with a goal prompt and monitor iterative planning and execution outputs

Platform

  • mac
  • windows
  • linux

Aurora's Core Features & Benefits

The Core Features

  • LLM-driven planning
  • Executor module for task execution
  • Tool integration layer for APIs and functions
  • Memory management for context retention
  • Dynamic re-planning capabilities
  • Customizable prompt templates

The Benefits

  • Accelerated agent development
  • Modular and extensible architecture
  • Seamless API integration
  • Improved automation accuracy
  • Open-source community support

Aurora's Main Use Cases & Applications

  • Automated research assistants
  • Dynamic content generation workflows
  • Customer support chatbots with external API access
  • Data analysis and reporting agents

FAQs of Aurora

Aurora Company Information

Aurora Reviews

5/5
Do You Recommend Aurora? Leave a Comment Below!

Aurora's Main Competitors and alternatives?

  • LangChain Agents
  • AutoGPT
  • BabyAGI
  • Microsoft Semantic Kernel

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