> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Llama Index Workflows

[LlamaIndex Workflows](https://www.llamaindex.ai/blog/introducing-workflows-beta-a-new-way-to-create-complex-ai-applications-with-llamaindex) are a subset of the LlamaIndex package specifically designed to support agent development.

Our [LlamaIndexInstrumentor](/future-agi/products/observability/auto-instrumentation/llamaindex) automatically captures traces for LlamaIndex Workflows agents. If you've already enabled that instrumentor, you do not need to complete the steps below.

## 1. Installation

First install the traceAI and necessary llama-index packages.

```bash theme={null}
pip install traceAI-llamaindex
pip install llama-index
```

***

## 2. Set Environment Variables

Set up your environment variables to authenticate with FutureAGI.

```python theme={null}
import os

os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
```

***

## 3. Initialize Trace Provider

Set up the trace provider to create a new project in FutureAGI, establish telemetry data pipelines .

```python theme={null}
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="openai_project",
)
```

***

## 4. Instrument your Project

Instrument your Project with LlamaIndex Instrumentor. This instrumentor will trace both LlamaIndex Workflows calls, as well as calls to the general LlamaIndex package.

```python theme={null}
from traceai_llamaindex import LlamaIndexInstrumentor

LlamaIndexInstrumentor().instrument(tracer_provider=trace_provider)
```

***

## 5. Run LlamaIndex Workflows

Run your LlamaIndex workflows as you normally would. Our Instrumentor will automatically trace and send the telemetry data to our platform.

```python theme={null}
import asyncio
from llama_index.core.workflow import (
    Event,
    StartEvent,
    StopEvent,
    Workflow,
    step,
)
from llama_index.llms.openai import OpenAI

class JokeEvent(Event):
    joke: str

class JokeFlow(Workflow):
    llm = OpenAI()

    @step
    async def generate_joke(self, ev: StartEvent) -> JokeEvent:
        topic = ev.topic

        prompt = f"Write your best joke about {topic}."
        response = await self.llm.acomplete(prompt)
        return JokeEvent(joke=str(response))

    @step
    async def critique_joke(self, ev: JokeEvent) -> StopEvent:
        joke = ev.joke

        prompt = f"Give a thorough analysis and critique of the following joke: {joke}"
        response = await self.llm.acomplete(prompt)
        return StopEvent(result=str(response))


async def main():
    w = JokeFlow(timeout=60, verbose=False)
    result = await w.run(topic="pirates")
    print(str(result))


if __name__ == "__main__":
    asyncio.run(main())
```