PraisonAI is a production-ready Multi AI Agents framework, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
git clone https://github.com/MervinPraison/PraisonAI.gitPraisonAI is a production-ready Multi AI Agents framework designed to facilitate the creation and management of AI agents that can automate tasks ranging from simple inquiries to complex problem-solving. This low-code solution emphasizes simplicity and customization, allowing users to build multi-agent systems that enhance human-agent collaboration. By leveraging PraisonAI, organizations can significantly improve their workflow automation processes, making it an ideal tool for developers, product managers, and AI practitioners looking to optimize their operations. One of the key benefits of using PraisonAI is its ability to save time and resources by automating repetitive tasks. With an implementation time of just 30 minutes, users can quickly deploy AI agents that handle multiple queries simultaneously, thus streamlining customer support. Additionally, the framework allows for the development of AI-driven personal assistants that can manage schedules and set reminders, ultimately enhancing productivity and freeing up valuable time for more strategic activities. PraisonAI is particularly suited for those in roles such as developers and product managers who are looking to implement AI automation solutions within their teams. Its medium GTM relevance indicates that it is a viable option for organizations aiming to adopt AI-first workflows. The framework's versatility is showcased in various use cases, including creating a multi-agent system for data analysis, where agents collaborate to extract insights from large datasets, and implementing a workflow automation tool that coordinates tasks between different AI agents to optimize project management. With a complexity rating of intermediate, PraisonAI requires a basic understanding of AI systems and low-code development. While the time savings are currently unspecified, the potential for increased efficiency and productivity is clear. By integrating PraisonAI into existing workflows, organizations can harness the power of AI agents to tackle diverse challenges, making it a valuable addition to any AI automation strategy.
Automate customer support inquiries using AI agents that can handle multiple queries simultaneously.
Create a multi-agent system for data analysis, where agents collaborate to extract insights from large datasets.
Develop an AI-driven personal assistant that can manage schedules, set reminders, and provide information on demand.
Implement a workflow automation tool that coordinates tasks between different AI agents to optimize project management.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/MervinPraison/PraisonAICopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a multi-agent AI system using PraisonAI to automate the process of [TASK]. Define the roles of each agent, including [AGENT_1_ROLE], [AGENT_2_ROLE], and [AGENT_3_ROLE]. Specify how these agents will interact to achieve the goal of [OUTCOME].
To automate the process of managing customer support inquiries, we can create a multi-agent AI system using PraisonAI. The first agent, 'Inquiry Filter', will categorize incoming support tickets based on urgency and type, such as 'Technical Issue', 'Billing Question', or 'General Inquiry'. The second agent, 'Response Generator', will draft replies based on the category assigned by the Inquiry Filter, pulling from a knowledge base for accurate information. Finally, the 'Follow-Up Scheduler' agent will set reminders for unresolved tickets and ensure that customer follow-ups occur within 24 hours. This collaboration allows for a streamlined support process, reducing response times and improving customer satisfaction. For instance, if a ticket is categorized as a 'Technical Issue', the Response Generator might suggest a solution based on previous similar inquiries, while the Follow-Up Scheduler ensures that the customer receives a follow-up email within a day to check if their issue is resolved.
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