PraisonAI 🦞

PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous 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

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🎯 Use Cases

AI agents solving real-world problems across industries:

Use Case	Description
🔍 Research & Analysis	Conduct deep research, gather information, and generate insights from multiple sources automatically
💻 Code Generation	Write, debug, and refactor code with AI agents that understand your codebase and requirements
✍️ Content Creation	Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams
📊 Data Pipelines	Extract, transform, and analyze data from APIs, databases, and web sources automatically
🤖 Customer Support	Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses
⚙️ Workflow Automation	Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct

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🚀 Meet your first Agent (Under 1 Minute)

1. Install the lightweight core SDK:

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

1. 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.")

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🌌 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).

Provider	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

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🌟 Why PraisonAI?

	Feature	How
🔌	MCP Protocol — stdio, HTTP, WebSocket, SSE	tools=MCP("npx ...")
🧠	Planning Mode — plan → execute → reason	planning=True
🔍	Deep Research — multi-step autonomous research	Docs
🤖	External Agents — orchestrate Claude Code, Gemini CLI, Codex	Docs
🔄	Agent Handoffs — seamless conversation passing	handoff=True
🛡️	Guardrails — input/output validation	Docs
	Web Search + Fetch — native browsing	web_search=True
🪞	Self Reflection — agent reviews its own output	Docs
🔀	Workflow Patterns — route, parallel, loop, repeat	Docs
🧠	Memory (zero deps) — works out of the box	memory=True

	Feature	How
💡	Prompt Caching — reduce latency + cost	prompt_caching=True
💾	Sessions + Auto-Save — persistent state across restarts	auto_save="my-project"
💭	Thinking Budgets — control reasoning depth	thinking_budget=1024
📚	RAG + Quality-Based RAG — auto quality scoring retrieval	Docs
📊	Model Router — auto-routes to cheapest capable model	Docs
🧊	Shadow Git Checkpoints — auto-rollback on failure	Docs
📡	A2A Protocol — agent-to-agent interop	Docs
📏	Context Compaction — never hit token limits	Docs
📡	Telemetry — OpenTelemetry traces, spans, metrics	Docs
📜	Policy Engine — declarative agent behavior control	Docs
🔄	Background Tasks — fire-and-forget agents	Docs
🔁	Doom Loop Detection — auto-recovery from stuck agents	Docs
🕸️	Graph Memory — Neo4j-style relationship tracking	Docs
🏖️	Sandbox Execution — isolated code execution	Docs
🖥️	Bot Gateway — multi-agent routing across channels	Docs

---

📘 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. 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

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

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📄 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

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🎯 CLI Quick Reference

Category	Commands
Execution	praisonai, --auto, --interactive, --chat
Research	research, --query-rewrite, --deep-research
Planning	--planning, --planning-tools, --planning-reasoning
Workflows	workflow run, workflow list, workflow auto
Memory	memory show, memory add, memory search, memory clear
Knowledge	knowledge add, knowledge query, knowledge list
Sessions	session list, session resume, session delete
Tools	tools list, tools info, tools search
MCP	mcp list, mcp create, mcp enable
Development	commit, docs, checkpoint, hooks
Scheduling	schedule start, schedule list, schedule stop

📖 Full CLI reference

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✨ Key Features

Feature	Code	Docs
Single Agent	Example	📖
Multi Agents	Example	📖
Auto Agents	Example	📖
Self Reflection AI Agents	Example	📖
Reasoning AI Agents	Example	📖
Multi Modal AI Agents	Example	📖

Feature	Code	Docs
Simple Workflow	Example	📖
Workflow with Agents	Example	📖
Agentic Routing (route())	Example	📖
Parallel Execution (parallel())	Example	📖
Loop over List/CSV (loop())	Example	📖
Evaluator-Optimizer (repeat())	Example	📖
Conditional Steps	Example	📖
Workflow Branching	Example	📖
Workflow Early Stop	Example	📖
Workflow Checkpoints	Example	📖

Feature	Code	Docs
Code Interpreter Agents	Example	📖
AI Code Editing Tools	Example	📖
External Agents (All)	Example	📖
Claude Code CLI	Example	📖
Gemini CLI	Example	📖
Codex CLI	Example	📖
Cursor CLI	Example	📖

Feature	Code	Docs
Memory (Short & Long Term)	Example	📖
File-Based Memory	Example	📖
Claude Memory Tool	Example	📖
Add Custom Knowledge	Example	📖
RAG Agents	Example	📖
Chat with PDF Agents	Example	📖
Data Readers (PDF, DOCX, etc.)	CLI	📖
Vector Store Selection	CLI	📖
Retrieval Strategies	CLI	📖
Rerankers	CLI	📖
Index Types (Vector/Keyword/Hybrid)	CLI	📖
Query Engines (Sub-Question, etc.)	CLI	📖

Feature	Code	Docs
Deep Research Agents	Example	📖
Query Rewriter Agent	Example	📖
Native Web Search	Example	📖
Built-in Search Tools	Example	📖
Unified Web Search	Example	📖
Web Fetch (Anthropic)	Example	📖

Feature	Code	Docs
Planning Mode	Example	📖
Planning Tools	Example	📖
Planning Reasoning	Example	📖
Prompt Chaining	Example	📖
Evaluator Optimiser	Example	📖
Orchestrator Workers	Example	📖

Feature	Code	Docs
Data Analyst Agent	Example	📖
Finance Agent	Example	📖
Shopping Agent	Example	📖
Recommendation Agent	Example	📖
Wikipedia Agent	Example	📖
Programming Agent	Example	📖
Math Agents	Example	📖
Markdown Agent	Example	📖
Prompt Expander Agent	Example	📖

Feature	Code	Docs
Image Generation Agent	Example	📖
Image to Text Agent	Example	📖
Video Agent	Example	📖
Camera Integration	Example	📖

Feature	Code	Docs
MCP Transports	Example	📖
WebSocket MCP	Example	📖
MCP Security	Example	📖
MCP Resumability	Example	📖
MCP Config Management	Docs	📖
LangChain Integrated Agents	Example	📖

Feature	Code	Docs
Guardrails	Example	📖
Human Approval	Example	📖
Rules & Instructions	Docs	📖

Feature	Code	Docs
Async & Parallel Processing	Example	📖
Parallelisation	Example	📖
Repetitive Agents	Example	📖
Agent Handoffs	Example	📖
Stateful Agents	Example	📖
Autonomous Workflow	Example	📖
Structured Output Agents	Example	📖
Model Router	Example	📖
Prompt Caching	Example	📖
Fast Context	Example	📖

Feature	Code	Docs
100+ Custom Tools	Example	📖
YAML Configuration	Example	📖
100+ LLM Support	Example	📖
Callback Agents	Example	📖
Hooks	Example	📖
Middleware System	Example	📖
Configurable Model	Example	📖
Rate Limiter	Example	📖
Injected Tool State	Example	📖
Shadow Git Checkpoints	Example	📖
Background Tasks	Example	📖
Policy Engine	Example	📖
Thinking Budgets	Example	📖
Output Styles	Example	📖
Context Compaction	Example	📖

Feature	Code	Docs
Sessions Management	Example	📖
Auto-Save Sessions	Docs	📖
History in Context	Docs	📖
Telemetry	Example	📖
Langfuse Tracing	Docs	📖
Project Docs (.praison/docs/)	Docs	📖
AI Commit Messages	Docs	📖
@Mentions in Prompts	Docs	📖

Feature	Code	Docs
Slash Commands	Example	📖
Autonomy Modes	Example	📖
Cost Tracking	Example	📖
Repository Map	Example	📖
Interactive TUI	Example	📖
Git Integration	Example	📖
Sandbox Execution	Example	📖
CLI Compare	Example	📖
Profile/Benchmark	Docs	📖
Auto Mode	Docs	📖
Init	Docs	📖
File Input	Docs	📖
Final Agent	Docs	📖
Max Tokens	Docs	📖

Feature	Code	Docs
Accuracy Evaluation	Example	📖
Performance Evaluation	Example	📖
Reliability Evaluation	Example	📖
Criteria Evaluation	Example	📖

Feature	Code	Docs
Skills Management	Example	📖
Custom Skills	Example	📖

Feature	Code	Docs
Agent Scheduler	Example	📖

---

💻 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 under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.

Performance Metric	PraisonAI
Avg Instantiation Time	3.77 μs

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⭐ Star History

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🔍 Langfuse Tracing

pip install "praisonai[langfuse]"
praisonai langfuse

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🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

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

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👥 Contributing

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

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❓ FAQ & Troubleshooting

Install the package:

pip install praisonaiagents

Ensure your API key is set:

export OPENAI_API_KEY=your_key_here

For other providers, see Models docs.

# Start Ollama server first
ollama serve

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

See Models docs for more details.

Use the db parameter:

from praisonaiagents import Agent, db

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

See Persistence docs for supported databases.

from praisonaiagents import Agent

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

See Memory docs for more options.

from praisonaiagents import Agent, Agents

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

See Agents docs for more examples.

from praisonaiagents import Agent, MCP

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

See MCP docs for all transport options.

Getting Help

• 📚 Full Documentation
• 🐛 Report Issues
• 💬 Discussions

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