Verified project record

MervinPraison/PraisonAI

PraisonAI is a self-hosted framework for building autonomous, self-improving AI agents with built-in memory, RAG, and multi-LLM support. It enables developers to create and deploy multi-agent systems that research, plan, and execute tasks across CLI, library, and web GUI interfaces.

Project overview

Project type: MCP · AI Agent · RAG
Use cases: Search & Research · Coding & Development · Automation
Deployment: Refer to project documentation
License: MIT

Best for

Developers, AI engineers, and data teams who need a low-boilerplate framework to build and deploy autonomous agents locally or self-hosted.

Key capabilities

Create and manage single or multiple autonomous agents that collaborate, handoff tasks, and execute complex workflows.
Connect agents to external tools and servers using stdio, HTTP, WebSocket, and SSE transports.
Provides short and long-term memory for agents with zero dependencies required out of the box.
Enables Retrieval-Augmented Generation, document chat (e.g. PDF), and custom knowledge integration with various retrieval strategies.
A web interface providing built-in pages for Chat, Agents, Memory, Knowledge, Channels, Guardrails, and Cron.
Autonomous agents for multi-step research and gathering insights from multiple sources.
Terminal-based interface for executing agents, research, planning, and managing memory, knowledge, and sessions.

Limitations and risks

An OpenAI API key is required by default for LLM calls unless configured otherwise.
There is a risk of code injection attacks if tool functions use eval(), exec(), or subprocess on LLM input.

Getting started

Install the framework via pip or npm, export your OpenAI API key, and instantiate the Agent class in a Python script to begin.

Evidence and sources

GitHub project description: PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute tasks. Deployed in 5 li…
README: PraisonAI 🦞 — **Hire a 24/7 AI Workforce.** Stop writing boilerplate and start shipping autonomous, self-improving agents that research, plan, and execute tasks across your apps.
README: from praisonaiagents import Agent agent = Agent(instructions="You are a helpful AI assistant") agent.start("Write a movie script about a robot in Mars")
README: research_agent = Agent(instructions="Research about AI") summarise_agent = Agent(instructions="Summarise research agent's findings") agents = Agents(agents=[research_agent, summar…
README: | 🔌 | **MCP Protocol** — stdio, HTTP, WebSocket, SSE | `tools=MCP("npx ...")` |

Reviewed at: 2026-07-16T19:51:30Z