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PraisonAI

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Basic Information

PraisonAI is a production-ready framework for building, orchestrating, and running multi-agent AI systems that automate and solve tasks of varying complexity. It is designed to let developers and power users create single or multiple cooperating agents with self-reflection, reasoning, memory, tool use and multimodal capabilities. The project provides SDKs and CLIs for Python and JavaScript, a low-code and no-code interface including YAML playbooks and an Auto Mode CLI, and integration points with other agent frameworks such as AG2 (formerly AutoGen) and CrewAI. It supports adding custom knowledge, connecting to vector stores and external tools, and routing work across specialized LLM calls. The README documents agent workflows, sequential and hierarchical processes, parallel execution, orchestration patterns, and agent evaluation and optimizer loops to support end-to-end agentic solutions.

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5292
App URL
https://github.com/MervinPraison/PraisonAI
Github Repository
https://github.com/MervinPraison/PraisonAI/blob/main/README.md

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App Details

Features
The repository documents and ships features focused on multi-agent workflows and extensibility. It lists automated agent creation, self-reflection and reasoning agents, multi-agent collaboration, multimodal agent support and short/long term memory. It supports RAG agents, chat-with-PDF, code interpreter agents, structured output and math agents, async and parallel processing, auto agents and mini agents. There is YAML configuration and a playbook system, CLI Auto Mode, Python and JavaScript SDK examples, and integrations with LangChain, Ollama, Groq, Crawl4AI and other tooling. The project advertises 100+ custom tools and 100+ supported LLMs, interactive UIs, internet search and real-time voice and call interfaces, plus video tutorials and development instructions.
Use Cases
PraisonAI helps teams accelerate creation of agent-powered automation by providing reusable patterns, orchestration primitives and tooling for agent coordination and memory. It reduces boilerplate with SDKs, a CLI and YAML playbooks so users can prototype single agents or complex multi-agent pipelines without building orchestration from scratch. Built-in patterns such as hierarchical managers, prompt chaining, routing, parallelization and iterative evaluator-optimizer loops make it easier to implement scalable workflows. Support for many models, tool integrations, vector stores and RAG enables practical retrieval and domain knowledge augmentation. No-code options, demo UIs and video tutorials lower the barrier for non-experts, while development notes and extras let engineers extend tools, add custom models and integrate with AG2 or CrewAI for production deployments.

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