PraisonAI 🦞

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 pip install praisonai

* export TAVILY_API_KEY=xxxxx

AI agents solving real-world problems across industries:

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.")

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]"

npm install praisonai

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

Example OpenAI Example Anthropic Example Google Gemini Example Ollama Example Groq Example DeepSeek Example xAI Grok Example Mistral Example Cohere Example Perplexity Example Fireworks Example Together AI Example OpenRouter Example HuggingFace Example Azure OpenAI Example AWS Bedrock Example Google Vertex Example Databricks Example Cloudflare Example AI21 Example Replicate Example SageMaker Example Moonshot Example vLLM Example

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

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

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()

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

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

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

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

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

Lightweight chat interface for your AI agents.

pip install "praisonai[ui]"
praisonai ui

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

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

📖 Full CLI reference

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');

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.

pip install "praisonai[langfuse]"
praisonai langfuse

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

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

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")
)

• 📚 Full Documentation
• 🐛 Report Issues
• 💬 Discussions