---
title: "Praison AI"
sidebarTitle: "Home"
description: "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."
icon: "house"
openGraph:
  url: "/"
seo:
  canonical: "/"
---
<div className="flex justify-start w-full max-w-xs sm:max-w-sm md:max-w-md">
  <img 
    src="images/praisonai-logo-black.png"
    alt="PraisonAI Logo"
    className="w-full h-auto rounded-lg shadow-md block dark:hidden"
    width="400"
    height="100"
  />
  <img 
    src="images/praisonai-logo-light.png"
    alt="PraisonAI Logo"
    className="w-full h-auto rounded-lg shadow-md hidden dark:block"
    width="400"
    height="100"
  />
</div>
<div style={{ display: 'flex', flexWrap: 'wrap', gap: '1rem' }}>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" />
  </div>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://img.shields.io/github/stars/MervinPraison/PraisonAI?style=social" alt="GitHub Stars" />
  </div>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
  </div>
</div>
<a href="https://trendshift.io/repositories/9130" target="_blank">
  <img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison/PraisonAI | Trendshift" />
</a>
## Key Features

<CardGroup cols={2}>
  <Card title="AI Agents Creation" icon="robot">
    Automated creation and management of AI agents with self-reflection capabilities
  </Card>
  <Card title="Framework Integration" icon="puzzle-piece">
    Seamless integration with CrewAI and AG2 frameworks
  </Card>
  <Card title="LLM Support" icon="brain">
    Support for 100+ Language Learning Models
  </Card>
  <Card title="Code Integration" icon="code">
    Chat with your entire codebase using advanced context understanding
  </Card>
  <Card title="Interactive UI" icon="desktop">
    Rich, interactive user interfaces for better control and monitoring
  </Card>
  <Card title="Configuration" icon="gear">
    YAML-based configuration for easy setup and customization
  </Card>
  <Card title="Tool Integration" icon="screwdriver-wrench">
    Custom tool integration for extended functionality
  </Card>
  <Card title="Search Capability" icon="magnifying-glass">
    Internet search using Crawl4AI and Tavily
  </Card>
</CardGroup>

## Install

<Tabs>
  <Tab title="Code">
    <Steps>
      <Step title="Install Package">
        ```bash
        pip install praisonaiagents
        ```
      </Step>
      <Step title="Set API Key">
        ```bash
        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
        ```
      </Step>
      <Step title="Create File">
        Create `app.py` file

        ## Code Example

<CodeGroup>
```python 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")
```

```python Multi Agents
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()
```
</CodeGroup>
      </Step>
      <Step title="Run Script">
        ```bash
        python app.py
        ```
      </Step>
    </Steps>
  </Tab>
  <Tab title="No Code">
    <Steps>
      <Step title="Install Package">
        Install the No Code PraisonAI Package:
        ```bash
        pip install praisonaiagents
        ```
      </Step>

      <Step title="Set API Key">
        ```bash
        export OPENAI_API_KEY=your_openai_key
        ```
      </Step>

      <Step title="Create Config">
        Create `agents.yaml`:

    <CodeGroup>
      ```yaml Single Agent
      roles:
        summarise_agent:
          instructions: Summarise Photosynthesis
      ```

      ```yaml Multiple Agents
      roles:
        diet_agent:
          instructions: Give me 5 healthy food recipes
        blog_agent:
          instructions: Write a blog post about the food recipes
      ```
    </CodeGroup>

<Note>
You can automatically create `agents.yaml` using:
```bash
praisonai --init "your task description"
```
</Note>

      </Step>

      <Step title="Run Agents">
        Execute your config:
        ```bash
        praisonai agents.yaml
        ```
      </Step>
    </Steps>
  </Tab>
  <Tab title="JavaScript">
    <Steps>
      <Step title="Install Package">
        ```bash
        npm install praisonai
        ```
      </Step>
      <Step title="Set API Key">
        ```bash
        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
        ```
      </Step>
      <Step title="Create File">
        Create `app.js` file

        ## Code Example

<CodeGroup>
```javascript Single Agent
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');
```

```javascript Multi Agents
const { Agent, PraisonAIAgents } = require('praisonai');

const researchAgent = new Agent({ instructions: 'Research about AI' });
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });

const agents = new PraisonAIAgents({ agents: [researchAgent, summariseAgent] });
agents.start();
```
</CodeGroup>
      </Step>
      <Step title="Run Script">
        ```bash
        node app.js
        ```
      </Step>
    </Steps>
  </Tab>
  <Tab title="TypeScript">
    <Steps>
      <Step title="Install Package">
        <CodeGroup>
        ```bash npm
        npm install praisonai
        ```
        ```bash yarn
        yarn add praisonai
        ```
        </CodeGroup>
      </Step>
      <Step title="Set API Key">
        ```bash
        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
        ```
      </Step>
      <Step title="Create File">
        Create `app.ts` file

        ## Code Example

<CodeGroup>
```javascript Single Agent
import { Agent } from 'praisonai';

const agent = new Agent({ 
  instructions: `You are a creative writer who writes short stories with emojis.`,
  name: "StoryWriter"
});

agent.start("Write a story about a time traveler")
```

```javascript Multi Agents
import { Agent, PraisonAIAgents } from 'praisonai';

const storyAgent = new Agent({
  instructions: "Generate a very short story (2-3 sentences) about artificial intelligence with emojis.",
  name: "StoryAgent"
});

const summaryAgent = new Agent({
  instructions: "Summarize the provided AI story in one sentence with emojis.",
  name: "SummaryAgent"
});

const agents = new PraisonAIAgents({
  agents: [storyAgent, summaryAgent]
});

agents.start()
```
</CodeGroup>
      </Step>
      <Step title="Run Script">
        ```bash
        npx ts-node app.ts
        ```
      </Step>
    </Steps>
  </Tab>
</Tabs>

## Playground

<iframe 
  id="codeExecutionFrame"
  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
  width="100%" 
  height="600px" 
  frameborder="0"
  allow="clipboard-read; clipboard-write"
  scrolling="yes"
  onload="resizeIframe(this)"
></iframe>

## AI Agents Flow

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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
```
<br />
<div className="relative w-full aspect-video overflow-hidden rounded-lg shadow-lg mb-8">
  <iframe
    className="absolute top-0 left-0 w-full h-full border-0"
    src="https://www.youtube.com/embed/Fn1lQjC0GO0"
    title="YouTube video player"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
    allowFullScreen
    style={{
      maxWidth: '100vw',
      margin: '0 auto'
    }}
  ></iframe>
</div>

## Integration Options

<AccordionGroup>
  <Accordion title="Ollama Integration">
    ```bash
    export OPENAI_BASE_URL=http://localhost:11434/v1
    ```
  </Accordion>
  <Accordion title="Groq Integration">
    ```bash
    export OPENAI_API_KEY=xxxxxxxxxxx
    export OPENAI_BASE_URL=https://api.groq.com/openai/v1
    ```
  </Accordion>
  <Accordion title="Logging Configuration">
    ```bash
    # Basic logging
    export LOGLEVEL=info
    
    # Advanced logging
    export LOGLEVEL=debug
    ```
  </Accordion>
</AccordionGroup>

## Use Cases

<CardGroup cols={2}>
  <Card title="Customer Service" icon="headset">
    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
  </Card>
  <Card title="Data Analysis" icon="chart-line">
    Create agents that can process, analyze, and derive insights from complex datasets.
  </Card>
  <Card title="Content Creation" icon="pen-nib">
    Deploy agents that can generate, edit, and optimize content across various formats.
  </Card>
  <Card title="Process Automation" icon="gears">
    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
  </Card>
</CardGroup>

## Praison AI Package Overall Features

<Frame caption="PraisonAI Features Overview">
  <div className="w-full max-w-4xl mx-auto">
    <img 
      src="images/architecture-light.png"
      alt="PraisonAI Features"
      className="w-full h-auto rounded-lg shadow-lg block dark:hidden"
      width="1200"
      height="800"
    />
    <img 
      src="images/architecture-dark.png"
      alt="PraisonAI Features"
      className="w-full h-auto rounded-lg shadow-lg hidden dark:block"
      width="1200"
      height="800"
    />
  </div>
</Frame>

## Features

<CardGroup cols={3}>
  <Card title="Self-Reflection" icon="brain-circuit" href="/features/selfreflection">
    Agents that evaluate and improve their own responses for higher accuracy
  </Card>
  <Card title="Reasoning" icon="gears" href="/features/reasoning">
    Multi-step logical reasoning and autonomous problem solving
  </Card>
  <Card title="CrewAI Framework" icon="users-gear" href="/framework/crewai">
    Build collaborative AI teams with CrewAI integration
  </Card>
  <Card title="AG2 Framework" icon="robot" href="/framework/autogen">
    Create autonomous agent networks using AG2 (Formerly AutoGen)
  </Card>
  <Card title="Multimodal Agents" icon="icons" href="/framework/multimodalagents">
    Work with agents that can process text, images, and other data types
  </Card>
  <Card title="Train" icon="graduation-cap" href="/train">
    Train and fine-tune your LLMs for specific tasks and domains. Then use it as an AI Agent.
  </Card>
</CardGroup>

## User Interfaces

<CardGroup cols={3}>
  <Card title="Multi Agents UI" icon="users" href="/ui/ui">
    Work with CrewAI or AG2 multi-agent systems
  </Card>
  <Card title="Chat Interface" icon="comments" href="/ui/chat">
    Chat with 100+ LLMs using a single AI Agent
  </Card>
  <Card title="Code Interface" icon="code" href="/ui/code">
    Interact with your entire codebase
  </Card>
</CardGroup>

<Note>
  Welcome to PraisonAI - Your comprehensive solution for building and managing multi-agent LLM systems with self-reflection capabilities.
</Note>

<div style={{ display: 'flex', flexWrap: 'wrap', gap: '1rem' }}>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" />
  </div>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" />
  </div>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" />
  </div>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://img.shields.io/github/stars/MervinPraison/PraisonAI?style=social" alt="GitHub Stars" />
  </div>
  <div className="hover:opacity-80 transition-opacity">
    <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
  </div>
</div>

## Video Tutorials

| Topic | Video |
|-------|--------|
| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
| Reasoning Data Generating Agent | [![Reasoning Data](https://img.youtube.com/vi/fUT332Y2zA8/0.jpg)](https://www.youtube.com/watch?v=fUT332Y2zA8) |
| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
| Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |