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

# BeeAI framework integration

> Send BeeAI Framework traces to Orq.ai using OpenTelemetry and OpenInference. Monitor agent workflows, tool calls, and LLM interactions.

<CardGroup cols={1}>
  <Card title="Observability" icon="chart-line" href="#observability">
    Instrument your code with OpenTelemetry to capture traces, logs, and metrics for every LLM call, agent step, and tool use.
  </Card>
</CardGroup>

## Observability

### Overview

BeeAI is IBM's open-source agent framework for building production-ready multi-agent systems. It uses the `openinference-instrumentation-beeai` library to export traces via OpenTelemetry.

### Prerequisites

* An Orq.ai account and [API Key](/docs/administer/api-keys)
* Python 3.10+
* An OpenAI API key (`OPENAI_API_KEY`) — the examples use OpenAI models

### Installation

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install beeai-framework \
    opentelemetry-api \
    opentelemetry-sdk \
    "opentelemetry-exporter-otlp-proto-http" \
    openinference-instrumentation-beeai
```

### Configuring Orq.ai Observability

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from openinference.instrumentation.beeai import BeeAIInstrumentor

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
BeeAIInstrumentor().instrument(tracer_provider=tracer_provider)
```

### Basic Example

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import os
import asyncio
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from openinference.instrumentation.beeai import BeeAIInstrumentor

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
BeeAIInstrumentor().instrument(tracer_provider=tracer_provider)

from beeai_framework.agents.react import ReActAgent
from beeai_framework.adapters.openai import OpenAIChatModel
from beeai_framework.memory import UnconstrainedMemory

async def main():
    agent = ReActAgent(
        llm=OpenAIChatModel("gpt-4o-mini"),
        memory=UnconstrainedMemory(),
        tools=[],
    )
    result = await agent.run("What is 2 + 2?")
    print(result.output.text)

asyncio.run(main())
```

## Evaluations & Experiments

Once your agents are running, use **Evaluatorq** to score outputs across a dataset and **Experiments** to compare configurations side-by-side.

<CardGroup cols={2}>
  <Card title="Run Evaluations with Evaluatorq" icon="flask" href="/docs/evaluators/build#evaluatorq">
    Run parallel evaluations across your agents and compare results.
  </Card>

  <Card title="Run Experiments via the API" icon="flask-vial" href="/docs/experiments/api">
    Compare agent configurations and view results in the AI Studio.
  </Card>
</CardGroup>