MCP Server

Active

MIT

PraisonAI MCP Server

io.github.MervinPraison/praisonai

Build autonomous, self-reflecting AI agents and connect them to any MCP server in minutes.

What is the PraisonAI MCP server?

PraisonAI is an open-source Python/JavaScript framework for building autonomous, self-reflecting AI agents and multi-agent workflows, with built-in Model Context Protocol (MCP) client support. It lets developers connect agents to external tools and MCP servers via stdio, HTTP, WebSocket, or SSE transports, and ships with a CLI, dashboard, and visual workflow builder. It supports 100+ LLM providers including OpenAI, Anthropic, Gemini, and local models via Ollama.

PraisonAI is an AI agents framework focused on shipping autonomous, self-improving agents with minimal code. It provides a core Python SDK (praisonaiagents), a CLI tool (praisonai), and optional add-ons for chat UI, visual workflow building (Flow), and multi-channel bot deployment (Claw, connecting to Telegram/Slack/Discord). A core feature is native MCP client support, letting agents connect to any MCP server over stdio, streamable HTTP, WebSocket, or SSE transports to gain access to external tools. It also includes features like planning mode, self reflection, agent handoffs, guardrails, memory, RAG, and support for 100+ LLM providers, making it suited for research, coding, content generation, data pipelines, customer support bots, and workflow automation.

How to install PraisonAI

Copy-paste configuration for popular MCP clients.

transport: stdio

Config generated by PluginBench — verify against the source before use.

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "praisonai": {
      "command": "uvx",
      "args": [
        "praisonai"
      ]
    }
  }
}

Cursor

~/.cursor/mcp.json

{
  "mcpServers": {
    "praisonai": {
      "command": "uvx",
      "args": [
        "praisonai"
      ]
    }
  }
}

Windsurf

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "praisonai": {
      "command": "uvx",
      "args": [
        "praisonai"
      ]
    }
  }
}

VS Code

.vscode/mcp.json

{
  "servers": {
    "praisonai": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "praisonai"
      ]
    }
  }
}

Claude Code

claude mcp add praisonai -- uvx praisonai

Use cases

Building single or multi-agent systems that research, plan, and execute tasks autonomously
Connecting agents to external MCP servers (stdio, HTTP, WebSocket, SSE) for tool access like search or memory
Deploying 24/7 AI customer support bots on Telegram, Slack, or Discord via the Claw dashboard
Automating multi-step business workflows with agent handoffs, self-reflection, and guardrails
Creating data pipelines that extract, transform, and analyze data from APIs and web sources

PraisonAI MCP server FAQ

What does the PraisonAI MCP server do?

PraisonAI is an AI agents framework that acts as an MCP client, letting agents built with it connect to and use tools exposed by any MCP server over stdio, HTTP, WebSocket, or SSE. It also provides its own agent orchestration features like planning, self-reflection, memory, and multi-agent handoffs.

Is PraisonAI free to use?

Yes, PraisonAI is open source under the MIT license and installable via pip (praisonai, praisonaiagents) or npm. You'll need your own API keys for whichever LLM provider (OpenAI, Anthropic, etc.) you choose to use.

How do I connect an MCP server to a PraisonAI agent?

Use the MCP() helper in Python, e.g. `Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))` for stdio servers, or pass an HTTP/WebSocket URL directly, optionally with an auth_token and environment variables.

What authentication is required?

PraisonAI itself requires an LLM provider API key (e.g. OPENAI_API_KEY) set as an environment variable. Individual MCP servers you connect to may require their own auth tokens or API keys, passed via the env parameter or auth_token argument.

What LLM providers does PraisonAI support?

It supports 100+ LLMs including OpenAI, Anthropic, Google Gemini, DeepSeek, Azure, Ollama, Groq, Mistral, Cerebras, Cohere, OpenRouter, Perplexity, AWS Bedrock, Vertex AI, HuggingFace, and more.

How do I install PraisonAI for use in Claude or Cursor?

Install the core SDK with `pip install praisonaiagents` (or `pip install praisonai` for the CLI), set your LLM API key as an environment variable, then define agents in Python and attach MCP tools using the MCP() helper as shown in the README examples.

README (reference)

Source of truth, from the repository.

PraisonAI 🦞

</div>

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

 ██████╗ ██████╗  █████╗ ██╗███████╗ ██████╗ ███╗   ██╗     █████╗ ██╗
 ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║    ██╔══██╗██║
 ██████╔╝██████╔╝███████║██║███████╗██║   ██║██╔██╗ ██║    ███████║██║
 ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║   ██║██║╚██╗██║    ██╔══██║██║
 ██║     ██║  ██║██║  ██║██║███████║╚██████╔╝██║ ╚████║    ██║  ██║██║
 ╚═╝     ╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝    ╚═╝  ╚═╝╚═╝

 pip install praisonai

* export TAVILY_API_KEY=xxxxx

🎯 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

🚀 Meet your first Agent (Under 1 Minute)

Install the lightweight core SDK:

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

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

<img src="https://img.shields.io/badge/OpenAI-412991?style=flat&logo=openai&logoColor=white" alt="OpenAI" /> <img src="https://img.shields.io/badge/Anthropic-191919?style=flat&logo=anthropic&logoColor=white" alt="Anthropic" /> <img src="https://img.shields.io/badge/Google_Gemini-4285F4?style=flat&logo=google&logoColor=white" alt="Google Gemini" /> <img src="https://img.shields.io/badge/DeepSeek-566AB2?style=flat" alt="DeepSeek" /> <img src="https://img.shields.io/badge/Azure-0078D4?style=flat&logo=microsoftazure&logoColor=white" alt="Azure" /> <img src="https://img.shields.io/badge/Ollama-000000?style=flat" alt="Ollama" /> <img src="https://img.shields.io/badge/Groq-F05237?style=flat" alt="Groq" /> <img src="https://img.shields.io/badge/Mistral-FF7000?style=flat" alt="Mistral" /> <img src="https://img.shields.io/badge/Cerebras-F05A28?style=flat" alt="Cerebras" /> <img src="https://img.shields.io/badge/Cohere-39594D?style=flat" alt="Cohere" /> <img src="https://img.shields.io/badge/OpenRouter-6467F2?style=flat" alt="OpenRouter" /> <img src="https://img.shields.io/badge/Perplexity-20808D?style=flat" alt="Perplexity" /> <img src="https://img.shields.io/badge/Fireworks-FF6B35?style=flat" alt="Fireworks" /> <img src="https://img.shields.io/badge/AWS_Bedrock-FF9900?style=flat&logo=amazonaws&logoColor=white" alt="AWS Bedrock" /> <img src="https://img.shields.io/badge/xAI_Grok-000000?style=flat" alt="xAI Grok" /> <img src="https://img.shields.io/badge/Vertex_AI-4285F4?style=flat&logo=googlecloud&logoColor=white" alt="Vertex AI" /> <img src="https://img.shields.io/badge/HuggingFace-FFD21E?style=flat&logo=huggingface&logoColor=black" alt="HuggingFace" /> <img src="https://img.shields.io/badge/Together_AI-000000?style=flat" alt="Together AI" /> <img src="https://img.shields.io/badge/Databricks-FF3621?style=flat&logo=databricks&logoColor=white" alt="Databricks" /> <img src="https://img.shields.io/badge/Replicate-262626?style=flat" alt="Replicate" /> <img src="https://img.shields.io/badge/Cloudflare-F38020?style=flat&logo=cloudflare&logoColor=white" alt="Cloudflare" /> <details> <summary>View all 24 providers with examples</summary>

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

</details> <div align="center"> <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank"> <img src=".github/images/elon_musk_praisonai.png" alt="Highlighted by Elon Musk" width="600" /> </a> "Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a> </div>

🌟 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`

<details> <summary>View all 25 features</summary>

	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

</details>

📘 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:

Variable	Required	Description
`OPENAI_API_KEY`	Yes	OpenAI API key for all LLM calls
`TAVILY_API_KEY`	Yes (Claw)	Tavily key for the built-in web-search tool. Get one free at https://app.tavily.com

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

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

✨ Key Features

<details open> <summary>🤖 Core Agents</summary>

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	📖

</details> <details> <summary>🔄 Workflows</summary>

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	📖

</details> <details> <summary>💻 Code & Development</summary>

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	📖

</details> <details> <summary>🧠 Memory & Knowledge</summary>

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	📖

</details> <details> <summary>🔬 Research & Intelligence</summary>

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	📖

</details> <details> <summary>📋 Planning & Execution</summary>

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

</details> <details> <summary>👥 Specialized Agents</summary>

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	📖

</details> <details> <summary>🎨 Media & Multimodal</summary>

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

</details> <details> <summary>🔌 Protocols & Integration</summary>

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

</details> <details> <summary>🛡️ Safety & Control</summary>

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

</details> <details> <summary>⚙️ Advanced Features</summary>

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	📖

</details> <details> <summary>🛠️ Tools & Configuration</summary>

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	📖

</details> <details> <summary>📊 Monitoring & Management</summary>

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	📖

</details> <details> <summary>🖥️ CLI Features</summary>

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	📖

</details> <details> <summary>🧪 Evaluation</summary>

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

</details> <details> <summary>🎯 Agent Skills</summary>

Feature	Code	Docs
Skills Management	Example	📖
Custom Skills	Example	📖

</details> <details> <summary>⏰ 24/7 Scheduling</summary>

Feature	Code	Docs
Agent Scheduler	Example	📖

</details>

💻 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.

Performance Metric	PraisonAI
Avg Instantiation Time	14 μs

⭐ Star History

🔍 Langfuse Tracing

pip install "praisonai[langfuse]"
praisonai langfuse

🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

<details> <summary>View all 22 video tutorials</summary>

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

</details>

👥 Contributing

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

❓ FAQ & Troubleshooting

<details> <summary>ModuleNotFoundError: No module named 'praisonaiagents'</summary>

Install the package:

pip install praisonaiagents

</details> <details> <summary>API key not found / Authentication error</summary>

Ensure your API key is set:

export OPENAI_API_KEY=your_key_here

For other providers, see Models docs.

</details> <details> <summary>How do I use a local model (Ollama)?</summary>

# Start Ollama server first
ollama serve

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

See Models docs for more details.

</details> <details> <summary>How do I persist conversations to a database?</summary>

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.

</details> <details> <summary>How do I enable agent memory?</summary>

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.

</details> <details> <summary>How do I run multiple agents together?</summary>

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.

</details> <details> <summary>How do I use MCP tools?</summary>

from praisonaiagents import Agent, MCP

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

See MCP docs for all transport options.

</details>

Getting Help

Apache-2.0

View repository →