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. From one agent to an entire organization, deployed in 5 lines of code.

curl -fsSL https://praison.ai/install.sh | bash

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 pip install praisonai

* export TAVILY_API_KEY=xxxxx

🎯 Use Cases

AI agents solving real-world problems across industries:

🚀 Meet your first Agent (Under 1 Minute)

1. Install the lightweight core SDK:

pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"

2. Run your first autonomous agent:

from praisonaiagents import Agent

# Give your agent a goal, and watch it work.
agent = Agent(instructions="You are a senior data analyst.")
agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")

🌌 The PraisonAI Ecosystem

Start simple with the core SDK, or expand to full visual builders and dashboards when you're ready.

• Core SDK (praisonaiagents): For pure Python development. pip install praisonaiagents
• 💻 PraisonAI CLI (praisonai): For terminal-based developers. pip install praisonai
• 🦞 Claw Dashboard: Connect agents directly to Telegram, Slack, or Discord. pip install "praisonai[claw]"
• 🔗 Flow Visual Builder: Drag-and-drop workflow creation. pip install "praisonai[flow]"
• 🤖 PraisonAI UI: Clean chat interface. pip install "praisonai[ui]"

JavaScript SDK

npm install praisonai

🧠 Supported Providers & Features

Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).

"Grok 3 customer support" — Elon Musk quoting PraisonAI's tutorial

🌟 Why 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:
    """Safely evaluate a numeric arithmetic expression."""
    import ast
    import operator
    
    # Define allowed operations
    _OPS = {
        ast.Add: operator.add,
        ast.Sub: operator.sub,
        ast.Mult: operator.mul,
        ast.Div: operator.truediv,
        ast.Pow: operator.pow,
        ast.USub: operator.neg,
        ast.UAdd: operator.pos,
    }
    
    def _safe_eval(node):
        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
            return node.value
        elif isinstance(node, ast.BinOp) and type(node.op) in _OPS:
            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
        elif isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
            return _OPS[type(node.op)](_safe_eval(node.operand))
        else:
            raise ValueError("Unsupported expression")
    
    try:
        return _safe_eval(ast.parse(expression, mode="eval").body)
    except (ValueError, SyntaxError, TypeError, ZeroDivisionError, OverflowError):
        raise ValueError("Invalid arithmetic expression")

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

⚠️ Security Note: Never use eval(), exec(), or subprocess in tool functions that process LLM-generated or user-supplied input. Always validate and sanitize inputs to prevent code injection attacks. 📖 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. PraisonAI Claw 🦞 (Dashboard UI)

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

pip install "praisonai[claw]"
praisonai claw

Required Environment Variables

Copy .env.example to .env and configure the following variables:

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

7. Langflow Integration 🔗 (Visual Flow Builder)

Build multi-agent workflows visually with drag-and-drop components in Langflow.

pip install "praisonai[flow]"
praisonai flow

Open http://localhost:7861 — use the Agent and Agent Team components to create sequential or parallel workflows. Connect Chat Input → Agent Team → Chat Output for instant multi-agent pipelines.

📖 Full Flow docs — visual agent building, component reference, and deployment

8. PraisonAI UI 🤖 (Clean Chat)

Lightweight chat interface for your AI agents.

pip install "praisonai[ui]"
praisonai ui

📄 Using YAML (No Code)

Example 1: Two Agents Working Together

Create agents.yaml:

framework: praisonai
topic: "Write a blog post about AI"

agents:
  researcher:
    role: Research Analyst
    goal: Research AI trends and gather information
    instructions: "Find accurate information about AI trends"
    
  writer:
    role: Content Writer
    goal: Write engaging blog posts
    instructions: "Write clear, engaging content based on research"

Run with:

praisonai agents.yaml

The agents automatically work together sequentially

Example 2: Agent with Custom Tool

Create two files in the same folder:

agents.yaml:

framework: praisonai
topic: "Calculate the sum of 25 and 15"

agents:
  calculator_agent:
    role: Calculator
    goal: Perform calculations
    instructions: "Use the add_numbers tool to help with calculations"
    tools:
      - add_numbers

tools.py:

def add_numbers(a: float, b: float) -> float:
    """
    Add two numbers together.
    
    Args:
        a: First number
        b: Second number
    
    Returns:
        The sum of a and b
    """
    return a + b

Run with:

praisonai agents.yaml

💡 Tips:

• Use the function name (e.g., add_numbers) in the tools list, not the file name
• Tools in tools.py are automatically discovered
• The function's docstring helps the AI understand how to use it

🎯 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');

⚡ Performance

PraisonAI is built for speed, with agent instantiation in around 14μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.

⭐ Star History

🔍 Langfuse Tracing

pip install "praisonai[langfuse]"
praisonai langfuse

🎓 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