> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Bluejay
> Simulation, observability, and evaluation platform for voice AI agents with native Pipecat integration.
## Overview
[Bluejay](https://getbluejay.ai) is a simulation, observability, and evaluation platform purpose-built for voice AI agents. It provides no-code simulation testing and production call monitoring that integrate directly with Pipecat, whether you're running on Pipecat Cloud or self-hosting.
With Bluejay, you can:
* Run automated simulations that call your agent and evaluate its responses
* Define test scenarios covering edge cases like interruptions, unexpected input, and multi-turn flows
* Monitor every production call with automated quality scoring
* Track evaluation metrics over time to catch regressions early
## Pipecat Cloud integration
If your agent is deployed on [Pipecat Cloud](/pipecat-cloud/introduction), Bluejay offers two zero-configuration integration paths:
Enter your Pipecat Cloud API key and agent name in Bluejay's dashboard.
Bluejay connects directly to your agent's API to spin up simulation sessions
with no code changes required.
Enter your agent's phone number into Bluejay and start running simulations
immediately. Bluejay calls your agent just like a real user would, testing
end-to-end behavior over telephony.
The telephony integration tests the full call stack, from phone network to
your agent and back, making it ideal for catching issues that only surface in
real call conditions.
## Self-hosted integration
If you're running Pipecat on your own infrastructure, Bluejay integrates via a WebSocket connection. Point Bluejay at your agent's WebSocket endpoint and it will establish a session to run simulations against your agent directly.
See the [Bluejay WebSocket integration guide](https://docs.getbluejay.ai/simulation-integrations/websockets) for setup instructions.
## Observability
Simulations cover pre-deployment testing, but observability ensures your agent maintains quality with real users. Bluejay's [Evaluate API](https://docs.getbluejay.ai/api-reference/endpoint/evaluate) lets you submit any production call for automated evaluation.
```python theme={null}
import requests
url = "https://api.getbluejay.ai/v1/evaluate"
headers = {"X-API-Key": ""}
payload = {
"agent_id": "",
"start_time_utc": "2025-03-31T18:30:00Z",
"participants": [
{"role": "AGENT", "name": "Healthcare Agent Harry"},
{"role": "USER", "name": "John Doe"},
],
"recording_url": "https://s3.amazonaws.com/my-recordings/call-123.wav",
}
response = requests.post(url, json=payload, headers=headers)
```
Integrate the evaluate endpoint into your agent's session cleanup logic to
automatically evaluate every production call without manual intervention.
## Traces
Bluejay supports [tracing](https://docs.getbluejay.ai/core-concepts/traces) to monitor and observe your agent's execution flow, latency, and performance in real-time. Traces conform to the [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/) standard, so you can use any compatible instrumentation library, including OpenInference, Langfuse, and OpenLLMetry.
To send traces to Bluejay:
1. Instrument your application to export traces to Bluejay's OTLP endpoint
2. Link traces to call evaluations by including the `trace_id` in your [Evaluate API](https://docs.getbluejay.ai/api-reference/endpoint/evaluate) requests
3. View traces alongside your call evaluations in the Bluejay dashboard
### Example: OpenTelemetry setup
Configure the OpenTelemetry SDK to export traces to Bluejay:
```python theme={null}
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
endpoint = "https://otlp.getbluejay.ai/v1/traces"
resource = Resource.create({SERVICE_NAME: "my-pipecat-agent"})
tracer_provider = trace_sdk.TracerProvider(resource=resource)
headers = {
"X-API-KEY": "",
}
tracer_provider.add_span_processor(
SimpleSpanProcessor(OTLPSpanExporter(endpoint, headers=headers))
)
```
Once the tracer provider is configured, use it with any OpenTelemetry-compatible instrumentation. For example, to automatically trace LLM calls:
```python theme={null}
from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
```
## Next steps
Full setup guides, API reference, and configuration options.
Step-by-step guide for connecting Bluejay to your Pipecat agent.
Learn about evaluation strategies for Pipecat agents.
Capture conversation transcripts to use with evaluation tools.