PraisonAI

PraisonAI 🦞 — Automate and solve complex challenges with AI agent teams that plan, research, code, and deliver results to Telegram, Discord, and WhatsApp — running 24/7. A low-code, production-ready multi-agent framework with handoffs, guardrails, memory, RAG, and 100+ LLM providers, built around simplicity, customisation, and effective human-agent collaboration.

Quick Paths:

• 🆕 New here? → Quick Start (1 minute to first agent)
• 📦 Installing? → Installation
• 🐍 Python SDK? → Python Examples
• 🎯 CLI user? → CLI Quick Reference
• 🤝 Contributing? → Contributing

⚡ Performance

PraisonAI is built for speed, with agent instantiation in under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.

🎯 Use Cases

AI agents solving real-world problems across industries:

Supported Providers

PraisonAI supports 100+ LLM providers through seamless integration:

🌟 Why PraisonAI?

🚀 Quick Start

Get started with PraisonAI in under 1 minute:

# Install
pip install praisonaiagents

# Set API key
export OPENAI_API_KEY=your_key_here

# Create a simple agent
python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"

Next Steps: Single Agent Example | Multi Agents | Full Docs

📦 Installation

Python SDK

Lightweight package dedicated for coding:

pip install praisonaiagents

For the full framework with CLI support:

pip install praisonai

🦞 AgentClaw — full UI with bots, memory, knowledge, and gateway:

pip install "praisonai[claw]"
praisonai claw

JavaScript SDK

npm install praisonai

📘 Using Python Code

1. Single Agent

from praisonaiagents import Agent
agent = Agent(instructions="You are a helpful AI assistant")
agent.start("Write a movie script about a robot in Mars")

2. Multi Agents

from praisonaiagents import Agent, Agents

research_agent = Agent(instructions="Research about AI")
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = Agents(agents=[research_agent, summarise_agent])
agents.start()

3. MCP (Model Context Protocol)

from praisonaiagents import Agent, MCP

# stdio - Local NPX/Python servers
agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))

# Streamable HTTP - Production servers
agent = Agent(tools=MCP("https://api.example.com/mcp"))

# WebSocket - Real-time bidirectional
agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))

# With environment variables
agent = Agent(
    tools=MCP(
        command="npx",
        args=["-y", "@modelcontextprotocol/server-brave-search"],
        env={"BRAVE_API_KEY": "your-key"}
    )
)

📖 Full MCP docs — stdio, HTTP, WebSocket, SSE transports

4. Custom Tools

from praisonaiagents import Agent, tool

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

@tool
def calculate(expression: str) -> float:
    """Evaluate a math expression."""
    return eval(expression)

agent = Agent(
    instructions="You are a helpful assistant",
    tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")

📖 Full tools docs — BaseTool, tool packages, 100+ built-in tools

5. Persistence (Databases)

from praisonaiagents import Agent, db

agent = Agent(
    name="Assistant",
    db=db(database_url="postgresql://localhost/mydb"),
    session_id="my-session"
)
agent.chat("Hello!")  # Auto-persists messages, runs, traces

📖 Full persistence docs — PostgreSQL, MySQL, SQLite, MongoDB, Redis, and 20+ more

6. AgentClaw 🦞 (Dashboard UI)

Connect your AI agents to Telegram, Discord, Slack, WhatsApp and more — all from a single command.

pip install "praisonai[claw]"
praisonai claw

Open http://localhost:8082 — the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.

📖 Full Claw docs — platform tokens, CLI options, Docker, and YAML agent mode

🎯 CLI Quick Reference

📖 Full CLI reference

✨ Key Features

💻 Using JavaScript Code

npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

const { Agent } = require('praisonai');
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
agent.start('Write a movie script about a robot in Mars');

⭐ Star History

🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

View all 22 video tutorials

Topic	Video
AI Agents with Self Reflection
Reasoning Data Generating Agent
AI Agents with Reasoning
Multimodal AI Agents
AI Agents Workflow
Async AI Agents
Mini AI Agents
AI Agents with Memory
Repetitive Agents
Introduction
Tools Overview
Custom Tools
Firecrawl Integration
User Interface
Crawl4AI Integration
Chat Interface
Code Interface
Mem0 Integration
Training
Realtime Voice Interface
Call Interface
Reasoning Extract Agents

👥 Contributing

We welcome contributions! Fork the repo, create a branch, and submit a PR → Contributing Guide.

❓ FAQ & Troubleshooting

pip install praisonaiagents

export OPENAI_API_KEY=your_key_here

# Start Ollama server first
ollama serve

# Set environment variable
export OPENAI_BASE_URL=http://localhost:11434/v1

from praisonaiagents import Agent, db

agent = Agent(
    name="Assistant",
    db=db(database_url="postgresql://localhost/mydb"),
    session_id="my-session"
)

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory=True,  # Enables file-based memory (no extra deps!)
    user_id="user123"
)

from praisonaiagents import Agent, Agents

agent1 = Agent(instructions="Research topics")
agent2 = Agent(instructions="Summarize findings")
agents = Agents(agents=[agent1, agent2])
agents.start()

from praisonaiagents import Agent, MCP

agent = Agent(
    tools=MCP("npx @modelcontextprotocol/server-memory")
)

Getting Help

• 📚 Full Documentation
• 🐛 Report Issues
• 💬 Discussions