PraisonAI 4.5.43

PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration.

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.

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Quick Paths:

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

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⚡ Performance

PraisonAI Agents is the fastest AI agent framework for agent instantiation.

Framework	Avg Time (μs)	Relative
PraisonAI	3.77	1.00x (fastest)
OpenAI Agents SDK	5.26	1.39x
Agno	5.64	1.49x
PraisonAI (LiteLLM)	7.56	2.00x
PydanticAI	226.94	60.16x
LangGraph	4,558.71	1,209x

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🚀 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

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🌟 Why PraisonAI?

	Feature	How
⚡	Fastest framework — 3.77μs (1,209x faster than LangGraph)	Benchmarks
🦞	Dashboard UI — chat, agents, memory, knowledge, cron	pip install "praisonai[claw]"
🔌	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
🌐	100+ LLM Providers — OpenAI, Anthropic, Gemini, Ollama, Groq...	Models
🛡️	Guardrails — input/output validation	Docs
💾	20+ Databases — one-line persistence	db=db("postgresql://...")
🔎	Web Search + Fetch — native browsing	web_search=True
🪞	Self Reflection — agent reviews its own output	Docs
�	Workflow Patterns — route, parallel, loop, repeat	Docs
💡	Prompt Caching — reduce latency + cost	prompt_caching=True
🧠	Memory (zero deps) — works out of the box	memory=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
�	Messaging Bots — Telegram, Discord, Slack, WhatsApp	praisonai bot telegram
📊	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

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📦 Installation

Python SDK

Lightweight package dedicated for coding:

pip install praisonaiagents

For the full framework with CLI support:

pip install praisonai

🦞 Full Dashboard with bots, memory, knowledge, and gateway:

pip install "praisonai[claw]"

JavaScript SDK

npm install praisonai

Environment Variables

Variable	Required	Description
OPENAI_API_KEY	Yes*	OpenAI API key
ANTHROPIC_API_KEY	No	Anthropic Claude API key
GOOGLE_API_KEY	No	Google Gemini API key
GROQ_API_KEY	No	Groq API key
OPENAI_BASE_URL	No	Custom API endpoint (for Ollama, Groq, etc.)

*At least one LLM provider API key is required.

# Set your API key
export OPENAI_API_KEY=your_key_here

# For Ollama (local models)
export OPENAI_BASE_URL=http://localhost:11434/v1

# For Groq
export OPENAI_API_KEY=your_groq_key
export OPENAI_BASE_URL=https://api.groq.com/openai/v1

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✨ Key Features

🤖 Core Agents

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	📖

🔄 Workflows

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	📖

💻 Code & Development

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	📖

🧠 Memory & Knowledge

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	📖

🔬 Research & Intelligence

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	📖

📋 Planning & Execution

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

👥 Specialized Agents

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	📖

🎨 Media & Multimodal

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

🔌 Protocols & Integration

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

🛡️ Safety & Control

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

⚙️ Advanced Features

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	📖

🛠️ Tools & Configuration

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	📖

📊 Monitoring & Management

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

🖥️ CLI Features

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	📖

🧪 Evaluation

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

🎯 Agent Skills

Feature	Code	Docs
Skills Management	Example	📖
Custom Skills	Example	📖

⏰ 24/7 Scheduling

Feature	Code	Docs
Agent Scheduler	Example	📖

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🌐 Supported Providers

PraisonAI supports 100+ LLM providers through seamless integration:

View all 24 providers

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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📘 Using Python Code

1. Single Agent

from praisonaiagents import Agent
agent = Agent(instructions="Your 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

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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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💻 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');

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⭐ Star History

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🎓 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

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👥 Contributing

We welcome contributions from the community! Here's how you can contribute:

1. Fork on GitHub - Use the "Fork" button on the repository page
2. Clone your fork - git clone https://github.com/yourusername/praisonAI.git
3. Create a branch - git checkout -b new-feature
4. Make changes and commit - git commit -am "Add some feature"
5. Push to your fork - git push origin new-feature
6. Submit a pull request - Via GitHub's web interface
7. Await feedback - From project maintainers

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🔧 Development

Using uv

# Install uv if you haven't already
pip install uv

# Install from requirements
uv pip install -r pyproject.toml

# Install with extras
uv pip install -r pyproject.toml --extra code
uv pip install -r pyproject.toml --extra "crewai,autogen"

Bump and Release

# From project root - bumps version and releases in one command
python src/praisonai/scripts/bump_and_release.py 2.2.99

# With praisonaiagents dependency
python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169

# Then publish
cd src/praisonai && uv publish

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❓ FAQ & Troubleshooting

ModuleNotFoundError: No module named 'praisonaiagents'

Install the package:

pip install praisonaiagents

API key not found / Authentication error

Ensure your API key is set:

export OPENAI_API_KEY=your_key_here

For other providers, see Environment Variables.

How do I use a local model (Ollama)?

# Start Ollama server first
ollama serve

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

See Models docs for more details.

How do I persist conversations to a database?

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.

How do I enable agent memory?

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.

How do I run multiple agents together?

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.

How do I use MCP tools?

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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Made with ❤️ by the PraisonAI Team

Documentation • GitHub • Issues