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PraisonAI

Build, coordinate, and run multi‑AI agents with low‑code simplicity

AI Agent Frameworks

PraisonAI provides a production‑ready framework for creating, managing, and orchestrating multiple LLM agents, featuring self‑reflection, memory, tool integration, and both code‑first and no‑code interfaces.

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Overview

Overview

PraisonAI is a production‑ready framework that lets developers and AI teams build, manage, and orchestrate multiple large‑language‑model agents. It emphasizes low‑code simplicity while supporting advanced capabilities such as self‑reflection, short‑ and long‑term memory, and multimodal reasoning.

Capabilities & Deployment

The library ships as a Python package (praisonaiagents) and a JavaScript SDK (praisonai), with a CLI for no‑code usage. Agents can be defined programmatically, via YAML configuration, or auto‑generated with the --auto flag. Integrated tools exceed 100 custom utilities, including code execution, web search, PDF handling, and RAG pipelines. Compatibility with over 100 LLM providers and LangChain ensures flexible model selection.

Getting Started

Install with pip install praisonaiagents or npm install praisonai, set your OPENAI_API_KEY, and launch single or multi‑agent workflows in minutes. Whether you need a research summarizer, a code‑interpreter, or a memory‑enabled chatbot, PraisonAI streamlines the end‑to‑end development cycle.

Highlights

Automated multi‑agent creation with self‑reflection
Short‑term and long‑term memory across agents
100+ built‑in tools and LangChain integration
Low‑code SDKs plus CLI auto‑mode for rapid prototyping

Pros

  • Supports both Python and JavaScript ecosystems
  • Extensive toolset enables diverse actions
  • Flexible workflow designs (sequential, hierarchical, conditional)
  • Simple installation and environment configuration

Considerations

  • Requires an OpenAI API key for many models
  • Learning curve for complex workflow orchestration
  • Limited graphical UI; primarily code/CLI driven
  • Performance depends on external LLM latency

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Fit guide

Great for

  • Teams building AI assistants that need multi‑step reasoning
  • Developers prototyping agent pipelines quickly
  • Enterprises integrating RAG and code execution into workflows
  • Researchers experimenting with self‑reflective agents

Not ideal when

  • Projects that require a full graphical UI for agent design
  • Environments without internet access to LLM APIs
  • Use cases demanding on‑premise model hosting only
  • Simple single‑prompt bots where a full framework adds overhead

How teams use it

Automated research summarization

Agents research a topic, summarize findings, and produce a concise report without manual intervention.

Code generation and execution

A code‑interpreter agent writes, runs, and validates Python scripts based on user prompts.

Customer support chatbot with memory

Multi‑agent system retains conversation context across sessions, providing personalized assistance.

Document analysis with RAG

Agents ingest PDFs, retrieve relevant passages, and answer questions using vector store integration.

Tech snapshot

Python95%
TypeScript4%
Shell1%
JavaScript1%
Dockerfile1%
Ruby1%

Tags

ai-agents-sdkaiai-agent-frameworkaiagentsmulti-agentsaiagentframeworkmulti-ai-agentagentsaiagentsframeworkmulti-agentai-framworkaiagentframeworkmulti-agent-systemsai-agentsmulti-agent-collaborationmulti-agent-systemai-agent-sdkai-agents-frameworkmulti-ai-agents

Frequently asked questions

What languages are supported?

PraisonAI offers SDKs for Python (pip) and JavaScript/Node.js (npm), plus a CLI for no‑code usage.

Do I need an OpenAI API key?

An API key is required for OpenAI models and many hosted LLMs; other providers can be configured via LangChain.

How do I define agent workflows?

Workflows can be described in code using the PraisonAIAgents class, via YAML configuration files, or generated automatically with the CLI’s --auto flag.

Can agents use external tools?

Yes, the framework includes over 100 built‑in tools and allows custom tool integration for actions like web search, code execution, and formatting.

Is there support for memory across tasks?

Agents provide short‑term and long‑term memory options, optionally backed by vector databases for persistent context.

Project at a glance

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