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940 changes: 652 additions & 288 deletions docs/docs.json

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6 changes: 3 additions & 3 deletions docs/concepts/agents.mdx → docs/en/concepts/agents.mdx
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Expand Up @@ -71,7 +71,7 @@ There are two ways to create agents in CrewAI: using **YAML configuration (recom

Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.

After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.

<Note>
Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
Expand Down Expand Up @@ -312,7 +312,7 @@ multimodal_agent = Agent(

<Note>
When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
</Note>
</Note>

<Note>
When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
Expand Down Expand Up @@ -425,7 +425,7 @@ strict_agent = Agent(
```python Code
# Perfect for document processing
document_processor = Agent(
role="Document Analyst",
role="Document Analyst",
goal="Extract insights from large research papers",
backstory="Expert at analyzing extensive documentation",
respect_context_window=True, # Handle large documents gracefully
Expand Down
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8 changes: 4 additions & 4 deletions docs/concepts/crews.mdx → docs/en/concepts/crews.mdx
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Expand Up @@ -45,7 +45,7 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm

Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.

After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
After creating your CrewAI project as outlined in the [Installation](/en/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.

#### Example Crew Class with Decorators

Expand All @@ -66,8 +66,8 @@ class YourCrewName:
# To see an example agent and task defined in YAML, checkout the following:
# - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
# - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'

@before_kickoff
def prepare_inputs(self, inputs):
Expand Down Expand Up @@ -111,7 +111,7 @@ class YourCrewName:
def crew(self) -> Crew:
return Crew(
agents=self.agents, # Automatically collected by the @agent decorator
tasks=self.tasks, # Automatically collected by the @task decorator.
tasks=self.tasks, # Automatically collected by the @task decorator.
process=Process.sequential,
verbose=True,
)
Expand Down
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4 changes: 2 additions & 2 deletions docs/concepts/tasks.mdx → docs/en/concepts/tasks.mdx
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Expand Up @@ -66,7 +66,7 @@ There are two ways to create tasks in CrewAI: using **YAML configuration (recomm

Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.

After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.

<Note>
Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
Expand Down Expand Up @@ -277,7 +277,7 @@ formatted_task = Task(

When `markdown=True`, the agent will receive additional instructions to format the output using:
- `#` for headers
- `**text**` for bold text
- `**text**` for bold text
- `*text*` for italic text
- `-` or `*` for bullet points
- `` `code` `` for inline code
Expand Down
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Expand Up @@ -10,11 +10,11 @@ icon: "people-arrows"

## Getting Started

<iframe
<iframe
width="100%"
height="400"
src="https://www.youtube.com/embed/-kSOTtYzgEw"
title="Building Crews with CrewAI CLI"
title="Building Crews with CrewAI CLI"
frameborder="0"
style={{ borderRadius: '10px' }}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
Expand All @@ -23,13 +23,13 @@ icon: "people-arrows"

### Installation and Setup

<Card title="Follow Standard Installation" icon="wrench" href="/installation">
<Card title="Follow Standard Installation" icon="wrench" href="/en/installation">
Follow our standard installation guide to set up CrewAI CLI and create your first project.
</Card>

### Building Your Crew

<Card title="Quickstart Tutorial" icon="rocket" href="/quickstart">
<Card title="Quickstart Tutorial" icon="rocket" href="/en/quickstart">
Follow our quickstart guide to create your first agent crew using YAML configuration.
</Card>

Expand All @@ -40,4 +40,4 @@ For Enterprise-specific support or questions, contact our dedicated support team

<Card title="Schedule a Demo" icon="calendar" href="mailto:support@crewai.com">
Book time with our team to learn more about Enterprise features and how they can benefit your organization.
</Card>
</Card>
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Expand Up @@ -122,7 +122,7 @@ The CrewAI CLI offers several commands to manage your deployments:

# Remove a deployment
crewai deploy remove <deployment_id>
```
```

## Option 2: Deploy Directly via Web Interface

Expand All @@ -132,14 +132,14 @@ You can also deploy your crews directly through the CrewAI Enterprise web interf

<Step title="Pushing to GitHub">

You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/quickstart).
You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/en/quickstart).

</Step>

<Step title="Connecting GitHub to CrewAI Enterprise">

1. Log in to [CrewAI Enterprise](https://app.crewai.com)
2. Click on the button "Connect GitHub"
2. Click on the button "Connect GitHub"

<Frame>
![Connect GitHub Button](/images/enterprise/connect-github.png)
Expand Down Expand Up @@ -201,20 +201,20 @@ For security reasons, the following environment variable naming patterns are **a

**Blocked Patterns:**
- Variables ending with `_TOKEN` (e.g., `MY_API_TOKEN`)
- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`)
- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`)
- Variables ending with `_SECRET` (e.g., `API_SECRET`)
- Variables ending with `_KEY` in certain contexts

**Specific Blocked Variables:**
- `GITHUB_USER`, `GITHUB_TOKEN`
- `AWS_REGION`, `AWS_DEFAULT_REGION`
- `AWS_REGION`, `AWS_DEFAULT_REGION`
- Various internal CrewAI system variables

### Allowed Exceptions

Some variables are explicitly allowed despite matching blocked patterns:
- `AZURE_AD_TOKEN`
- `AZURE_OPENAI_AD_TOKEN`
- `AZURE_OPENAI_AD_TOKEN`
- `ENTERPRISE_ACTION_TOKEN`
- `CREWAI_ENTEPRISE_TOOLS_TOKEN`

Expand All @@ -228,7 +228,7 @@ OPENAI_TOKEN=sk-...
DATABASE_PASSWORD=mypassword
API_SECRET=secret123

# ✅ Use these naming patterns instead
# ✅ Use these naming patterns instead
OPENAI_API_KEY=sk-...
DATABASE_CREDENTIALS=mypassword
API_CONFIG=secret123
Expand Down
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Expand Up @@ -56,8 +56,8 @@ CrewAI Enterprise extends the power of the open-source framework with features d
<Steps>
<Step title="Sign up for an account">
Create your account at [app.crewai.com](https://app.crewai.com)
<Card
title="Sign Up"
<Card
title="Sign Up"
icon="user"
href="https://app.crewai.com/signup"
>
Expand All @@ -66,34 +66,34 @@ CrewAI Enterprise extends the power of the open-source framework with features d
</Step>
<Step title="Build your first crew">
Use code or Crew Studio to build your crew
<Card
title="Build Crew"
<Card
title="Build Crew"
icon="paintbrush"
href="/enterprise/guides/build-crew"
href="/en/enterprise/guides/build-crew"
>
Build Crew
</Card>
</Step>
<Step title="Deploy your crew">
Deploy your crew to the Enterprise platform
<Card
title="Deploy Crew"
<Card
title="Deploy Crew"
icon="rocket"
href="/enterprise/guides/deploy-crew"
href="/en/enterprise/guides/deploy-crew"
>
Deploy Crew
</Card>
</Step>
<Step title="Access your crew">
Integrate with your crew via the generated API endpoints
<Card
title="API Access"
<Card
title="API Access"
icon="code"
href="/enterprise/guides/use-crew-api"
href="/en/enterprise/guides/kickoff-crew"
>
Use the Crew API
</Card>
</Step>
</Steps>

For detailed instructions, check out our [deployment guide](/enterprise/guides/deploy-crew) or click the button below to get started.
For detailed instructions, check out our [deployment guide](/en/enterprise/guides/deploy-crew) or click the button below to get started.
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Expand Up @@ -48,12 +48,12 @@ icon: "circle-question"

To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.

For detailed implementation guidance, see our [Human-in-the-Loop guide](/how-to/human-in-the-loop).
For detailed implementation guidance, see our [Human-in-the-Loop guide](/en/how-to/human-in-the-loop).
</Accordion>

<Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
CrewAI provides a range of advanced customization options:

- **Language Model Customization**: Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`)
- **Performance and Debugging Settings**: Adjust an agent's performance and monitor its operations
- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization
Expand Down Expand Up @@ -129,12 +129,12 @@ icon: "circle-question"

Here's a tutorial on how to consistently get structured outputs from your agents:
<Frame>
<iframe
<iframe
height="400"
width="100%"
src="https://www.youtube.com/embed/dNpKQk5uxHw"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
src="https://www.youtube.com/embed/dNpKQk5uxHw"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
</Frame>
</Accordion>
Expand All @@ -148,4 +148,4 @@ icon: "circle-question"
<Accordion title="How can you control the maximum number of requests per minute that the entire crew can perform?">
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
</Accordion>
</AccordionGroup>
</AccordionGroup>
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Expand Up @@ -44,7 +44,7 @@ Based on your agent configuration, CrewAI adds different default instructions:
"I MUST use these formats, my job depends on it!"
```

#### For Agents With Tools
#### For Agents With Tools
```text
"IMPORTANT: Use the following format in your response:
Expand Down Expand Up @@ -127,7 +127,7 @@ custom_prompt_template = """Task: {input}
Please complete this task thoughtfully."""

agent = Agent(
role="Research Assistant",
role="Research Assistant",
goal="Help users find accurate information",
backstory="You are a helpful research assistant.",
system_template=custom_system_template,
Expand Down Expand Up @@ -164,7 +164,7 @@ crew = Crew(
```python
agent = Agent(
role="Analyst",
goal="Analyze data",
goal="Analyze data",
backstory="Expert analyst",
use_system_prompt=False # Disables system prompt separation
)
Expand All @@ -174,13 +174,13 @@ agent = Agent(

For production transparency, integrate with observability platforms to monitor all prompts and LLM interactions. This allows you to see exactly what prompts (including default instructions) are being sent to your LLMs.

See our [Observability documentation](/how-to/observability) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.
See our [Observability documentation](/en/observability/overview) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.

### Best Practices for Production

1. **Always inspect generated prompts** before deploying to production
2. **Use custom templates** when you need full control over prompt content
3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/how-to/observability))
3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/en/observability/overview))
4. **Test with different LLMs** as default instructions may work differently across models
5. **Document your prompt customizations** for team transparency

Expand Down Expand Up @@ -313,4 +313,4 @@ Low-level prompt customization in CrewAI opens the door to super custom, complex

<Check>
You now have the foundation for advanced prompt customizations in CrewAI. Whether you're adapting for model-specific structures or domain-specific constraints, this low-level approach lets you shape agent interactions in highly specialized ways.
</Check>
</Check>
Original file line number Diff line number Diff line change
Expand Up @@ -448,5 +448,5 @@ Congratulations! You now understand the principles and practices of effective ag
## Next Steps
- Experiment with different agent configurations for your specific use case
- Learn about [building your first crew](/guides/crews/first-crew) to see how agents work together
- Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced orchestration
- Learn about [building your first crew](/en/guides/crews/first-crew) to see how agents work together
- Explore [CrewAI Flows](/en/guides/flows/first-flow) for more advanced orchestration
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ When building AI applications with CrewAI, one of the most important decisions y
At the heart of this decision is understanding the relationship between **complexity** and **precision** in your application:

<Frame caption="Complexity vs. Precision Matrix for CrewAI Applications">
<img src="../../images/complexity_precision.png" alt="Complexity vs. Precision Matrix" />
<img src="/images/complexity_precision.png" alt="Complexity vs. Precision Matrix" />
</Frame>

This matrix helps visualize how different approaches align with varying requirements for complexity and precision. Let's explore what each quadrant means and how it guides your architectural choices.
Expand Down Expand Up @@ -497,7 +497,7 @@ You now have a framework for evaluating CrewAI use cases and choosing the right

## Next Steps

- Learn more about [crafting effective agents](/guides/agents/crafting-effective-agents)
- Explore [building your first crew](/guides/crews/first-crew)
- Dive into [mastering flow state management](/guides/flows/mastering-flow-state)
- Check out the [core concepts](/concepts/agents) for deeper understanding
- Learn more about [crafting effective agents](/en/guides/agents/crafting-effective-agents)
- Explore [building your first crew](/en/guides/crews/first-crew)
- Dive into [mastering flow state management](/en/guides/flows/mastering-flow-state)
- Check out the [core concepts](/en/concepts/agents) for deeper understanding
Original file line number Diff line number Diff line change
Expand Up @@ -32,9 +32,9 @@ Let's get started building your first crew!

Before starting, make sure you have:

1. Installed CrewAI following the [installation guide](/installation)
1. Installed CrewAI following the [installation guide](/en/installation)
2. Set up your LLM API key in your environment, following the [LLM setup
guide](/concepts/llms#setting-up-your-llm)
guide](/en/concepts/llms#setting-up-your-llm)
3. Basic understanding of Python

## Step 1: Create a New CrewAI Project
Expand All @@ -54,7 +54,7 @@ This will generate a project with the basic structure needed for your crew. The
- A main script to run the crew

<Frame caption="CrewAI Framework Overview">
<img src="../../images/crews.png" alt="CrewAI Framework Overview" />
<img src="/images/crews.png" alt="CrewAI Framework Overview" />
</Frame>


Expand Down Expand Up @@ -287,7 +287,7 @@ SERPER_API_KEY=your_serper_api_key
# Add your provider's API key here too.
```

See the [LLM Setup guide](/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
See the [LLM Setup guide](/en/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).

## Step 8: Install Dependencies

Expand Down Expand Up @@ -388,7 +388,7 @@ Now that you've built your first crew, you can:
2. Try more complex task structures and workflows
3. Implement custom tools to give your agents new capabilities
4. Apply your crew to different topics or problem domains
5. Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced workflows with procedural programming
5. Explore [CrewAI Flows](/en/guides/flows/first-flow) for more advanced workflows with procedural programming

<Check>
Congratulations! You've successfully built your first CrewAI crew that can research and analyze any topic you provide. This foundational experience has equipped you with the skills to create increasingly sophisticated AI systems that can tackle complex, multi-stage problems through collaborative intelligence.
Expand Down
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