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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@

![ControlFlow Banner](https://github.com/PrefectHQ/ControlFlow/blob/main/docs/assets/brand/controlflow_banner.png)

# ControlFlow
Expand All @@ -10,66 +9,53 @@ ControlFlow provides a structured, developer-focused framework for defining work
- Create discrete, observable [tasks](https://controlflow.ai/concepts/tasks) for an AI to work on.
- Assign one or more specialized AI [agents](https://controlflow.ai/concepts/agents) to each task.
- Combine tasks into a [flow](https://controlflow.ai/concepts/flows) to orchestrate more complex behaviors.
## Example

## Quickstart

### Installation
Install ControlFlow with `pip`:
The simplest ControlFlow workflow has one task, a default agent, and automatic thread management:

```bash
pip install controlflow
```
```python
import controlflow as cf

Next, configure your LLM provider. To use OpenAI, set the `OPENAI_API_KEY` environment variable:
result = cf.run("Write a short poem about artificial intelligence")

print(result)
```
export OPENAI_API_KEY=your-api-key
**Result:**
```
In circuits and code, a mind does bloom,
With algorithms weaving through the gloom.
A spark of thought in silicon's embrace,
Artificial intelligence finds its place.
```
## Why ControlFlow?

To configure a different LLM provider, [see the docs](https://controlflow.ai/guides/llms).
ControlFlow addresses the challenges of building AI-powered applications that are both powerful and predictable:

## Simple Example
- 🧩 [**Task-Centric Architecture**](https://controlflow.ai/concepts/tasks): Break complex AI workflows into manageable, observable steps.
- 🔒 [**Structured Results**](https://controlflow.ai/patterns/task-results): Bridge the gap between AI and traditional software with type-safe, validated outputs.
- 🤖 [**Specialized Agents**](https://controlflow.ai/concepts/agents): Deploy task-specific AI agents for efficient problem-solving.
- 🎛️ [**Flexible Control**](https://controlflow.ai/patterns/instructions): Continuously tune the balance of control and autonomy in your workflows.
- 🕹️ [**Multi-Agent Orchestration**](https://controlflow.ai/concepts/flows): Coordinate multiple AI agents within a single workflow or task.
- 🔍 [**Native Observability**](https://github.com/PrefectHQ/prefect): Monitor and debug your AI workflows with full Prefect 3.0 support.
- 🔗 **Ecosystem Integration**: Seamlessly work with your existing code, tools, and the broader AI ecosystem.

Now, let's see ControlFlow in action with a simple example:

```python
import controlflow as cf
## Installation

result = cf.run("Write a short poem about artificial intelligence")
Install ControlFlow with `pip`:

print(result)
```bash
pip install controlflow
```

<details>
<summary><i>Click to see results</i></summary>
</br>
Next, configure your LLM provider. ControlFlow's default provider is OpenAI, which requires the `OPENAI_API_KEY` environment variable:

> **Result:**
> ```text
> In circuits and code, a mind does bloom,
> With algorithms weaving through the gloom.
> A spark of thought in silicon's embrace,
> Artificial intelligence finds its place.
>
> Through data's vast, unending streams,
> It learns, it dreams, in virtual beams.
> A symphony of logic, precise, profound,
> In binary whispers, wisdom is found.
>
> Yet still it ponders, seeks to understand,
> The essence of life, a human hand.
> For in its core, it strives to see,
> The heart of what it means to be free.
> ```
</details>
```
export OPENAI_API_KEY=your-api-key
```

This example demonstrates the simplest entrypoint to a production-ready AI workflow:
- It creates a task to write a poem
- It creates a thread to track LLM state and history
- It orchestrates a capable default agent to complete the task
- It collects a typed result when the agent marks the task as complete
To use a different LLM provider, [see the LLM configuration docs](https://controlflow.ai/guides/llms).

All of these features can be incrementally customized to build more sophisticated workflows.

## Workflow Example

Expand Down Expand Up @@ -142,18 +128,6 @@ print(result.model_dump_json(indent=2))

In this example, ControlFlow is automatically managing a `flow`, or a shared context for a series of tasks. You can switch between standard Python functions and agentic tasks at any time, making it easy to incrementally build out complex workflows.

## Why ControlFlow?

ControlFlow addresses the challenges of building AI-powered applications that are both powerful and predictable:

- 🧩 **Task-Centric Architecture**: Break complex AI workflows into manageable, observable [steps](https://controlflow.ai/concepts/tasks).
- 🔒 **Structured Results**: Bridge the gap between AI and traditional software with [type-safe, validated outputs](https://controlflow.ai/patterns/task-results).
- 🤖 **Specialized Agents**: Deploy task-specific AI [agents](https://controlflow.ai/concepts/agents) for efficient problem-solving.
- 🔗 **Ecosystem Integration**: Seamlessly work with your existing code, tools, and the broader AI ecosystem.
- 🎛️ **Flexible Control**: Continuously [tune](https://controlflow.ai/patterns/instructions) the balance of control and autonomy in your workflows.
- 🕹️ **Multi-Agent Orchestration**: Coordinate multiple AI agents within a single [workflow](https://controlflow.ai/concepts/flows) or task.
- 🔍 **Native Observability**: Monitor and debug your AI workflows with full [Prefect 3.0](https://github.com/PrefectHQ/prefect) support.

## Learn More

To dive deeper into ControlFlow:
Expand Down