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crewAI

Fri, 05 Sep 2025 15:27:04 +0800

crewAIInc/crewAI

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Fast and Flexible Multi-Agent Automation Framework

CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely independent of LangChain or other agent frameworks. It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.

CrewAI Crews: Optimize for autonomy and collaborative intelligence.
CrewAI Flows: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively

With over 100,000 developers certified through our community courses at learn.crewai.com, CrewAI is rapidly becoming the standard for enterprise-ready AI automation.

CrewAI Enterprise Suite

CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.

You can try one part of the suite the Crew Control Plane for free

Crew Control Plane Key Features:

Tracing & Observability: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
Unified Control Plane: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
Seamless Integrations: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
Advanced Security: Built-in robust security and compliance measures ensuring safe deployment and management.
Actionable Insights: Real-time analytics and reporting to optimize performance and decision-making.
24/7 Support: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
On-premise and Cloud Deployment Options: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.

CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient, intelligent automations.

Why CrewAI?

CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:

Standalone Framework: Built from scratch, independent of LangChain or any other agent framework.
High Performance: Optimized for speed and minimal resource usage, enabling faster execution.
Flexible Low Level Customization: Complete freedom to customize at both high and low levels - from overall workflows and system architecture to granular agent behaviors, internal prompts, and execution logic.
Ideal for Every Use Case: Proven effective for both simple tasks and highly complex, real-world, enterprise-grade scenarios.
Robust Community: Backed by a rapidly growing community of over 100,000 certified developers offering comprehensive support and resources.

CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance.

Getting Started

Setup and run your first CrewAI agents by following this tutorial.

Learning Resources

Learn CrewAI through our comprehensive courses:

Multi AI Agent Systems with CrewAI - Master the fundamentals of multi-agent systems
Practical Multi AI Agents and Advanced Use Cases - Deep dive into advanced implementations

Understanding Flows and Crews

CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:

Crews: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
- Natural, autonomous decision-making between agents
- Dynamic task delegation and collaboration
- Specialized roles with defined goals and expertise
- Flexible problem-solving approaches
Flows: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
- Fine-grained control over execution paths for real-world scenarios
- Secure, consistent state management between tasks
- Clean integration of AI agents with production Python code
- Conditional branching for complex business logic

The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:

Build complex, production-grade applications
Balance autonomy with precise control
Handle sophisticated real-world scenarios
Maintain clean, maintainable code structure

Getting Started with Installation

To get started with CrewAI, follow these simple steps:

1. Installation

Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses UV for dependency management and package handling, offering a seamless setup and execution experience.

First, install CrewAI:

`1`	`pip install crewai`

If you want to install the ‘crewai’ package along with its optional features that include additional tools for agents, you can do so by using the following command:

`1`	`pip install 'crewai[tools]'`

The command above installs the basic package and also adds extra components which require more dependencies to function.

Troubleshooting Dependencies

If you encounter issues during installation or usage, here are some common solutions:

Common Issues

ModuleNotFoundError: No module named ’tiktoken’
- Install tiktoken explicitly: pip install 'crewai[embeddings]'
- If using embedchain or other tools: pip install 'crewai[tools]'
Failed building wheel for tiktoken
- Ensure Rust compiler is installed (see installation steps above)
- For Windows: Verify Visual C++ Build Tools are installed
- Try upgrading pip: pip install --upgrade pip
- If issues persist, use a pre-built wheel: pip install tiktoken --prefer-binary

2. Setting Up Your Crew with the YAML Configuration

To create a new CrewAI project, run the following CLI (Command Line Interface) command:

`1`	`crewai create crew <project_name>`

This command creates a new project folder with the following structure:

my_project/
├── .gitignore
├── pyproject.toml
├── README.md
├── .env
└── src/
    └── my_project/
        ├── __init__.py
        ├── main.py
        ├── crew.py
        ├── tools/
        │   ├── custom_tool.py
        │   └── __init__.py
        └── config/
            ├── agents.yaml
            └── tasks.yaml

You can now start developing your crew by editing the files in the src/my_project folder. The main.py file is the entry point of the project, the crew.py file is where you define your crew, the agents.yaml file is where you define your agents, and the tasks.yaml file is where you define your tasks.

To customize your project, you can:

Modify src/my_project/config/agents.yaml to define your agents.
Modify src/my_project/config/tasks.yaml to define your tasks.
Modify src/my_project/crew.py to add your own logic, tools, and specific arguments.
Modify src/my_project/main.py to add custom inputs for your agents and tasks.
Add your environment variables into the .env file.

Example of a simple crew with a sequential process:

Instantiate your crew:

`1`	`crewai create crew latest-ai-development`

Modify the files as needed to fit your use case:

agents.yaml

# src/my_project/config/agents.yaml
researcher:
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering the latest
    developments in {topic}. Known for your ability to find the most relevant
    information and present it in a clear and concise manner.

reporting_analyst:
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} data analysis and research findings
  backstory: >
    You're a meticulous analyst with a keen eye for detail. You're known for
    your ability to turn complex data into clear and concise reports, making
    it easy for others to understand and act on the information you provide.

tasks.yaml

# src/my_project/config/tasks.yaml
research_task:
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
    the current year is 2025.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher

reporting_task:
  description: >
    Review the context you got and expand each topic into a full section for a report.
    Make sure the report is detailed and contains any and all relevant information.
  expected_output: >
    A fully fledge reports with the mains topics, each with a full section of information.
    Formatted as markdown without '```'
  agent: reporting_analyst
  output_file: report.md

crew.py

# src/my_project/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import List

@CrewBase
class LatestAiDevelopmentCrew():
	"""LatestAiDevelopment crew"""
	agents: List[BaseAgent]
	tasks: List[Task]

	@agent
	def researcher(self) -> Agent:
		return Agent(
			config=self.agents_config['researcher'],
			verbose=True,
			tools=[SerperDevTool()]
		)

	@agent
	def reporting_analyst(self) -> Agent:
		return Agent(
			config=self.agents_config['reporting_analyst'],
			verbose=True
		)

	@task
	def research_task(self) -> Task:
		return Task(
			config=self.tasks_config['research_task'],
		)

	@task
	def reporting_task(self) -> Task:
		return Task(
			config=self.tasks_config['reporting_task'],
			output_file='report.md'
		)

	@crew
	def crew(self) -> Crew:
		"""Creates the LatestAiDevelopment crew"""
		return Crew(
			agents=self.agents, # Automatically created by the @agent decorator
			tasks=self.tasks, # Automatically created by the @task decorator
			process=Process.sequential,
			verbose=True,
		)

main.py

#!/usr/bin/env python
# src/my_project/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew

def run():
    """
    Run the crew.
    """
    inputs = {
        'topic': 'AI Agents'
    }
    LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)

3. Running Your Crew

Before running your crew, make sure you have the following keys set as environment variables in your .env file:

An OpenAI API key (or other LLM API key): OPENAI_API_KEY=sk-...
A Serper.dev API key: SERPER_API_KEY=YOUR_KEY_HERE

Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:

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cd my_project
crewai install (Optional)

To run your crew, execute the following command in the root of your project:

`1`	`crewai run`

`1`	`python src/my_project/main.py`

If an error happens due to the usage of poetry, please run the following command to update your crewai package:

`1`	`crewai update`

You should see the output in the console and the report.md file should be created in the root of your project with the full final report.

In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. See more about the processes here.

Key Features

CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.

Standalone & Lean: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
Flexible & Precise: Easily orchestrate autonomous agents through intuitive Crews or precise Flows, achieving perfect balance for your needs.
Seamless Integration: Effortlessly combine Crews (autonomy) and Flows (precision) to create complex, real-world automations.
Deep Customization: Tailor every aspect—from high-level workflows down to low-level internal prompts and agent behaviors.
Reliable Performance: Consistent results across simple tasks and complex, enterprise-level automations.
Thriving Community: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance.

Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations.

Examples

You can test different real life examples of AI crews in the CrewAI-examples repo:

Quick Tutorial

Using Crews and Flows Together

CrewAI’s power truly shines when combining Crews with Flows to create sophisticated automation pipelines. CrewAI flows support logical operators like or_ and and_ to combine multiple conditions. This can be used with @start, @listen, or @router decorators to create complex triggering conditions.

or_: Triggers when any of the specified conditions are met.
and_Triggers when all of the specified conditions are met.

Here’s how you can orchestrate multiple Crews within a Flow:

from crewai.flow.flow import Flow, listen, start, router, or_
from crewai import Crew, Agent, Task, Process
from pydantic import BaseModel

# Define structured state for precise control
class MarketState(BaseModel):
    sentiment: str = "neutral"
    confidence: float = 0.0
    recommendations: list = []

class AdvancedAnalysisFlow(Flow[MarketState]):
    @start()
    def fetch_market_data(self):
        # Demonstrate low-level control with structured state
        self.state.sentiment = "analyzing"
        return {"sector": "tech", "timeframe": "1W"}  # These parameters match the task description template

    @listen(fetch_market_data)
    def analyze_with_crew(self, market_data):
        # Show crew agency through specialized roles
        analyst = Agent(
            role="Senior Market Analyst",
            goal="Conduct deep market analysis with expert insight",
            backstory="You're a veteran analyst known for identifying subtle market patterns"
        )
        researcher = Agent(
            role="Data Researcher",
            goal="Gather and validate supporting market data",
            backstory="You excel at finding and correlating multiple data sources"
        )

        analysis_task = Task(
            description="Analyze {sector} sector data for the past {timeframe}",
            expected_output="Detailed market analysis with confidence score",
            agent=analyst
        )
        research_task = Task(
            description="Find supporting data to validate the analysis",
            expected_output="Corroborating evidence and potential contradictions",
            agent=researcher
        )

        # Demonstrate crew autonomy
        analysis_crew = Crew(
            agents=[analyst, researcher],
            tasks=[analysis_task, research_task],
            process=Process.sequential,
            verbose=True
        )
        return analysis_crew.kickoff(inputs=market_data)  # Pass market_data as named inputs

    @router(analyze_with_crew)
    def determine_next_steps(self):
        # Show flow control with conditional routing
        if self.state.confidence > 0.8:
            return "high_confidence"
        elif self.state.confidence > 0.5:
            return "medium_confidence"
        return "low_confidence"

    @listen("high_confidence")
    def execute_strategy(self):
        # Demonstrate complex decision making
        strategy_crew = Crew(
            agents=[
                Agent(role="Strategy Expert",
                      goal="Develop optimal market strategy")
            ],
            tasks=[
                Task(description="Create detailed strategy based on analysis",
                     expected_output="Step-by-step action plan")
            ]
        )
        return strategy_crew.kickoff()

    @listen(or_("medium_confidence", "low_confidence"))
    def request_additional_analysis(self):
        self.state.recommendations.append("Gather more data")
        return "Additional analysis required"

This example demonstrates how to:

Use Python code for basic data operations
Create and execute Crews as steps in your workflow
Use Flow decorators to manage the sequence of operations
Implement conditional branching based on Crew results

Connecting Your Crew to a Model

CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.

Please refer to the Connect CrewAI to LLMs page for details on configuring your agents’ connections to models.

How CrewAI Compares

CrewAI’s Advantage: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control.

LangGraph: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework’s tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems.

P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example (see comparison) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example (detailed analysis).

Autogen: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents’ interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
ChatDev: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.

Contribution

CrewAI is open-source and we welcome contributions. If you’re looking to contribute, please:

Fork the repository.
Create a new branch for your feature.
Add your feature or improvement.
Send a pull request.
We appreciate your input!

Installing Dependencies

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uv lock
uv sync

Virtual Env

uv venv

Pre-commit hooks

`1`	`pre-commit install`

Running Tests

`1`	`uv run pytest .`

Running static type checks

`1`	`uvx mypy src`

Packaging

`1`	`uv build`

Installing Locally

`1`	`pip install dist/*.tar.gz`

Telemetry

CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.

It’s pivotal to understand that NO data is collected concerning prompts, task descriptions, agents’ backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the share_crew feature is enabled, detailed data including task descriptions, agents’ backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.

Data collected includes:

Version of CrewAI
- So we can understand how many users are using the latest version
Version of Python
- So we can decide on what versions to better support
General OS (e.g. number of CPUs, macOS/Windows/Linux)
- So we know what OS we should focus on and if we could build specific OS related features
Number of agents and tasks in a crew
- So we make sure we are testing internally with similar use cases and educate people on the best practices
Crew Process being used
- Understand where we should focus our efforts
If Agents are using memory or allowing delegation
- Understand if we improved the features or maybe even drop them
If Tasks are being executed in parallel or sequentially
- Understand if we should focus more on parallel execution
Language model being used
- Improved support on most used languages
Roles of agents in a crew
- Understand high level use cases so we can build better tools, integrations and examples about it
Tools names available
- Understand out of the publicly available tools, which ones are being used the most so we can improve them

Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the share_crew attribute to True on their Crews. Enabling share_crew results in the collection of detailed crew and task execution data, including goal, backstory, context, and output of tasks. This enables a deeper insight into usage patterns while respecting the user’s choice to share.

License

CrewAI is released under the MIT License.

Frequently Asked Questions (FAQ)

General

Features and Capabilities

Resources and Community

Enterprise Features

Q: What exactly is CrewAI?

A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.

Q: How do I install CrewAI?

A: Install CrewAI using pip:

`1`	`pip install crewai`

For additional tools, use:

`1`	`pip install 'crewai[tools]'`

Q: Does CrewAI depend on LangChain?

A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.

Q: Can CrewAI handle complex use cases?

A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.

Q: Can I use CrewAI with local AI models?

A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the LLM Connections documentation for more details.

Q: What makes Crews different from Flows?

A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.

Q: How is CrewAI better than LangChain?

A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.

Q: Is CrewAI open-source?

A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.

Q: Does CrewAI collect data from users?

A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.

Q: Where can I find real-world CrewAI examples?

A: Check out practical examples in the CrewAI-examples repository, covering use cases like trip planners, stock analysis, and job postings.

Q: How can I contribute to CrewAI?

A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.

Q: What additional features does CrewAI Enterprise offer?

A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.

Q: Is CrewAI Enterprise available for cloud and on-premise deployments?

A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.

Q: Can I try CrewAI Enterprise for free?

A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the Crew Control Plane for free.

Q: Does CrewAI support fine-tuning or training custom models?

A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.

Q: Can CrewAI agents interact with external tools and APIs?

A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.

Q: Is CrewAI suitable for production environments?

A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.

Q: How scalable is CrewAI?

A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.

Q: Does CrewAI offer debugging and monitoring tools?

A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.

Q: What programming languages does CrewAI support?

A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.

Q: Does CrewAI offer educational resources for beginners?

A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.

Q: Can CrewAI automate human-in-the-loop workflows?

A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.

agents

Thu, 10 Jul 2025 15:31:08 +0800

ed-donner/agents

Master AI Agentic Engineering - build autonomous AI Agents

6 week journey to code and deploy AI Agents with OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP

If you’re looking at this in Cursor, please right click on the filename in the Explorer on the left, and select “Open preview”, to view the formatted version.

I couldn’t be more excited to welcome you! This is the start of your 6 week adventure into the powerful, astonishing and often surreal world of Agentic AI.

Before you begin

I’m here to help you be most successful! Please do reach out if I can help, either in the platform or by emailing me direct (ed@edwarddonner.com). It’s always great to connect with people on LinkedIn to build up the community - you’ll find me here:
https://www.linkedin.com/in/eddonner/
And this is new to me, but I’m also trying out X/Twitter at @edwarddonner - if you’re on X, please show me how it’s done 😂

The not-so-dreaded setup instructions

Perhaps famous last words: but I really, truly hope that I’ve put together an environment that will be not too horrific to set up!

Windows people, your instructions are here
Mac people, yours are here
Linux people, yours are here

Any problems, please do contact me.

Important notes for CrewAI week (Week 3)

Windows PC users: you will need to have checked the “gotcha #4” at the top of the SETUP-PC instructions – installing Microsoft Build Tools.
If you don’t do this, then CrewAI will fail with an obscure error involving Chroma..

Then, you will need to run this command in a Cursor Terminal in the project root directory in order to run the Crew commands:
uv tool install crewai
And in case you’ve used Crew before, it might be worth doing this to make sure you have the latest:
uv tool upgrade crewai

Then please keep in mind for Crew:

There are two ways that you can work on the CrewAI project in week 3. Either review the code for each project while I build it, and then do crewai run to see it in action. Or if you prefer to be more hands-on, then create your own Crew project from scratch to mirror mine; for example, create my_debate to go alongside debate, and write the code alongside me. Either approach works!
Windows users: there’s a new issue that was recently introduced by one of Crew’s libraries. Until this is fixed, you might get a “unicode” error when you try to run crewai create crew. If that happens, please try running this command in the Terminal first: $env:PYTHONUTF8 = "1"
Gemini users: in addition to a key in your .env file for GOOGLE_API_KEY, you will need an identical key for GEMINI_API_KEY

Super useful resources

The course resources with videos
Many essential guides in the guides section
The troubleshooting notebook

API costs - please read me!

This course does involve making calls to OpenAI and other frontier models, requiring an API key and a small spend, which we set up in the SETUP instructions. If you’d prefer not to spend on API calls, there are cheaper alternatives like DeepSeek and free alternatives like using Ollama!

Details are here.

Be sure to monitor your API costs to ensure you are totally happy with any spend. For OpenAI, the dashboard is here.

ABOVE ALL ELSE -

Be sure to have fun with the course! You could not have picked a better time to be learning about Agentic AI. I hope you enjoy every single minute! And if you get stuck at any point - contact me.

PraisonAI

Tue, 03 Jun 2025 15:32:36 +0800

MervinPraison/PraisonAI

Praison AI

PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.

Key Features

🤖 Automated AI Agents Creation
🔄 Self Reflection AI Agents
🧠 Reasoning AI Agents
👁️ Multi Modal AI Agents
🤝 Multi Agent Collaboration
🎭 AI Agent Workflow
📚 Add Custom Knowledge
🧠 Agents with Short and Long Term Memory
📄 Chat with PDF Agents
💻 Code Interpreter Agents
📚 RAG Agents
🤔 Async & Parallel Processing
🔄 Auto Agents
🔢 Math Agents
🎯 Structured Output Agents
🔗 LangChain Integrated Agents
📞 Callback Agents
🤏 Mini AI Agents
🛠️ 100+ Custom Tools
📄 YAML Configuration
💯 100+ LLM Support

Using Python Code

Light weight package dedicated for coding:

`1`	`pip install praisonaiagents`

`1`	`export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx`

1. Single Agent

Create app.py file and add the code below:

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

Run:

`1`	`python app.py`

2. Multi Agents

Create app.py file and add the code below:

from praisonaiagents import Agent, PraisonAIAgents

research_agent = Agent(instructions="Research about AI")
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
agents.start()

Run:

`1`	`python app.py`

Using No Code

Auto Mode:

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3

pip install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
praisonai --auto create a movie script about Robots in Mars

Using JavaScript Code

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npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

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3

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');

AI Agents Flow

  graph LR
    %% Define the main flow
    Start([▶ Start]) --> Agent1
    Agent1 --> Process[⚙ Process]
    Process --> Agent2
    Agent2 --> Output([✓ Output])
    Process -.-> Agent1
    
    %% Define subgraphs for agents and their tasks
    subgraph Agent1[ ]
        Task1[📋 Task]
        AgentIcon1[🤖 AI Agent]
        Tools1[🔧 Tools]
        
        Task1 --- AgentIcon1
        AgentIcon1 --- Tools1
    end
    
    subgraph Agent2[ ]
        Task2[📋 Task]
        AgentIcon2[🤖 AI Agent]
        Tools2[🔧 Tools]
        
        Task2 --- AgentIcon2
        AgentIcon2 --- Tools2
    end

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Start,Output,Task1,Task2 input
    class Process,AgentIcon1,AgentIcon2 process
    class Tools1,Tools2 tools
    class Agent1,Agent2 transparent

AI Agents with Tools

Create AI agents that can use tools to interact with external systems and perform actions.

  flowchart TB
    subgraph Tools
        direction TB
        T3[Internet Search]
        T1[Code Execution]
        T2[Formatting]
    end

    Input[Input] ---> Agents
    subgraph Agents
        direction LR
        A1[Agent 1]
        A2[Agent 2]
        A3[Agent 3]
    end
    Agents ---> Output[Output]

    T3 --> A1
    T1 --> A2
    T2 --> A3

    style Tools fill:#189AB4,color:#fff
    style Agents fill:#8B0000,color:#fff
    style Input fill:#8B0000,color:#fff
    style Output fill:#8B0000,color:#fff

AI Agents with Memory

Create AI agents with memory capabilities for maintaining context and information across tasks.

  flowchart TB
    subgraph Memory
        direction TB
        STM[Short Term]
        LTM[Long Term]
    end

    subgraph Store
        direction TB
        DB[(Vector DB)]
    end

    Input[Input] ---> Agents
    subgraph Agents
        direction LR
        A1[Agent 1]
        A2[Agent 2]
        A3[Agent 3]
    end
    Agents ---> Output[Output]

    Memory <--> Store
    Store <--> A1
    Store <--> A2
    Store <--> A3

    style Memory fill:#189AB4,color:#fff
    style Store fill:#2E8B57,color:#fff
    style Agents fill:#8B0000,color:#fff
    style Input fill:#8B0000,color:#fff
    style Output fill:#8B0000,color:#fff

AI Agents with Different Processes

Sequential Process

The simplest form of task execution where tasks are performed one after another.

  graph LR
    Input[Input] --> A1
    subgraph Agents
        direction LR
        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
    end
    A3 --> Output[Output]

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Input,Output input
    class A1,A2,A3 process
    class Agents transparent

Hierarchical Process

Uses a manager agent to coordinate task execution and agent assignments.

  graph TB
    Input[Input] --> Manager
    
    subgraph Agents
        Manager[Manager Agent]
        
        subgraph Workers
            direction LR
            W1[Worker 1]
            W2[Worker 2]
            W3[Worker 3]
        end
        
        Manager --> W1
        Manager --> W2
        Manager --> W3
    end
    
    W1 --> Manager
    W2 --> Manager
    W3 --> Manager
    Manager --> Output[Output]

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Input,Output input
    class Manager,W1,W2,W3 process
    class Agents,Workers transparent

Workflow Process

Advanced process type supporting complex task relationships and conditional execution.

  graph LR
    Input[Input] --> Start
    
    subgraph Workflow
        direction LR
        Start[Start] --> C1{Condition}
        C1 --> |Yes| A1[Agent 1]
        C1 --> |No| A2[Agent 2]
        A1 --> Join
        A2 --> Join
        Join --> A3[Agent 3]
    end
    
    A3 --> Output[Output]

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Input,Output input
    class Start,A1,A2,A3,Join process
    class C1 decision
    class Workflow transparent

Agentic Routing Workflow

Create AI agents that can dynamically route tasks to specialized LLM instances.

  flowchart LR
    In[In] --> Router[LLM Call Router]
    Router --> LLM1[LLM Call 1]
    Router --> LLM2[LLM Call 2]
    Router --> LLM3[LLM Call 3]
    LLM1 --> Out[Out]
    LLM2 --> Out
    LLM3 --> Out
    
    style In fill:#8B0000,color:#fff
    style Router fill:#2E8B57,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Agentic Orchestrator Worker

Create AI agents that orchestrate and distribute tasks among specialized workers.

  flowchart LR
    In[In] --> Router[LLM Call Router]
    Router --> LLM1[LLM Call 1]
    Router --> LLM2[LLM Call 2]
    Router --> LLM3[LLM Call 3]
    LLM1 --> Synthesizer[Synthesizer]
    LLM2 --> Synthesizer
    LLM3 --> Synthesizer
    Synthesizer --> Out[Out]
    
    style In fill:#8B0000,color:#fff
    style Router fill:#2E8B57,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Synthesizer fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Agentic Autonomous Workflow

Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.

  flowchart LR
    Human[Human] <--> LLM[LLM Call]
    LLM -->|ACTION| Environment[Environment]
    Environment -->|FEEDBACK| LLM
    LLM --> Stop[Stop]
    
    style Human fill:#8B0000,color:#fff
    style LLM fill:#2E8B57,color:#fff
    style Environment fill:#8B0000,color:#fff
    style Stop fill:#333,color:#fff

Agentic Parallelization

Create AI agents that can execute tasks in parallel for improved performance.

  flowchart LR
    In[In] --> LLM2[LLM Call 2]
    In --> LLM1[LLM Call 1]
    In --> LLM3[LLM Call 3]
    LLM1 --> Aggregator[Aggregator]
    LLM2 --> Aggregator
    LLM3 --> Aggregator
    Aggregator --> Out[Out]
    
    style In fill:#8B0000,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Aggregator fill:#fff,color:#000
    style Out fill:#8B0000,color:#fff

Agentic Prompt Chaining

Create AI agents with sequential prompt chaining for complex workflows.

  flowchart LR
    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
    Gate -->|Fail| Exit[Exit]
    
    style In fill:#8B0000,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
    style Exit fill:#8B0000,color:#fff

Agentic Evaluator Optimizer

Create AI agents that can generate and optimize solutions through iterative feedback.

  flowchart LR
    In[In] --> Generator[LLM Call Generator] 
    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
    Evaluator -->|REJECTED + FEEDBACK| Generator
    
    style In fill:#8B0000,color:#fff
    style Generator fill:#2E8B57,color:#fff
    style Evaluator fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Repetitive Agents

Create AI agents that can efficiently handle repetitive tasks through automated loops.

  flowchart LR
    In[Input] --> LoopAgent[("Looping Agent")]
    LoopAgent --> Task[Task]
    Task --> |Next iteration| LoopAgent
    Task --> |Done| Out[Output]
    
    style In fill:#8B0000,color:#fff
    style LoopAgent fill:#2E8B57,color:#fff,shape:circle
    style Task fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Adding Models

Ollama Integration

`1`	`export OPENAI_BASE_URL=http://localhost:11434/v1`

Groq Integration

Replace xxxx with Groq API KEY:

1
2

export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://api.groq.com/openai/v1

No Code Options

Agents Playbook

Simple Playbook Example

Create agents.yaml file and add the code below:

framework: praisonai
topic: Artificial Intelligence
roles:
  screenwriter:
    backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
    goal: Create scripts from concepts.
    role: Screenwriter
    tasks:
      scriptwriting_task:
        description: "Develop scripts with compelling characters and dialogue about {topic}."
        expected_output: "Complete script ready for production."

To run the playbook:

`1`	`praisonai agents.yaml`

Use 100+ Models

https://docs.praison.ai/models/

Development:

Below is used for development only.

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"

Contributing

Fork on GitHub: Use the “Fork” button on the repository page.
Clone your fork: git clone https://github.com/yourusername/praisonAI.git
Create a branch: git checkout -b new-feature
Make changes and commit: git commit -am "Add some feature"
Push to your fork: git push origin new-feature
Submit a pull request via GitHub’s web interface.
Await feedback from project maintainers.

Other Features

🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
💻 Chat with ENTIRE Codebase
🎨 Interactive UIs
📄 YAML-based Configuration
🛠️ Custom Tool Integration
🔍 Internet Search Capability (using Crawl4AI and Tavily)
🖼️ Vision Language Model (VLM) Support
🎙️ Real-time Voice Interaction

Star History

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

CrewAI on Producthunt daily

crewAI

crewAIInc/crewAI

Fast and Flexible Multi-Agent Automation Framework

CrewAI Enterprise Suite

Crew Control Plane Key Features:

Table of contents

Why CrewAI?

Getting Started

Understanding Flows and Crews

Getting Started with Installation

1. Installation

Troubleshooting Dependencies

Common Issues

2. Setting Up Your Crew with the YAML Configuration

To customize your project, you can:

Example of a simple crew with a sequential process:

3. Running Your Crew

Key Features

Examples

Quick Tutorial

Write Job Descriptions

Trip Planner

Stock Analysis

Using Crews and Flows Together

Connecting Your Crew to a Model

How CrewAI Compares

Contribution

Installing Dependencies

Virtual Env

Pre-commit hooks

Running Tests

Running static type checks

Packaging

Installing Locally

Telemetry

License

Frequently Asked Questions (FAQ)

General

Features and Capabilities

Resources and Community

Enterprise Features

Q: What exactly is CrewAI?

Q: How do I install CrewAI?

Q: Does CrewAI depend on LangChain?

Q: Can CrewAI handle complex use cases?

Q: Can I use CrewAI with local AI models?

Q: What makes Crews different from Flows?

Q: How is CrewAI better than LangChain?

Q: Is CrewAI open-source?

Q: Does CrewAI collect data from users?

Q: Where can I find real-world CrewAI examples?

Q: How can I contribute to CrewAI?

Q: What additional features does CrewAI Enterprise offer?

Q: Is CrewAI Enterprise available for cloud and on-premise deployments?

Q: Can I try CrewAI Enterprise for free?

Q: Does CrewAI support fine-tuning or training custom models?

Q: Can CrewAI agents interact with external tools and APIs?

Q: Is CrewAI suitable for production environments?

Q: How scalable is CrewAI?

Q: Does CrewAI offer debugging and monitoring tools?

Q: What programming languages does CrewAI support?

Q: Does CrewAI offer educational resources for beginners?

Q: Can CrewAI automate human-in-the-loop workflows?

agents

ed-donner/agents

Master AI Agentic Engineering - build autonomous AI Agents

6 week journey to code and deploy AI Agents with OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP

Before you begin

The not-so-dreaded setup instructions

Important notes for CrewAI week (Week 3)

Super useful resources

API costs - please read me!

ABOVE ALL ELSE -

PraisonAI

MervinPraison/PraisonAI

Praison AI

Key Features

Using Python Code

1. Single Agent