OpenTelemetry Support for fast-agent

Getting Started

fast-agent supports OpenTelemetry, providing observability of MCP and LLM interactions. This is also a useful test/eval tool for comparing the behaviour of MCP Servers with different mixes of Tools, descriptions and models.

OpenTelemetry example

Set up an OpenTelemetry server

The first step is to set up an OpenTelemetry server. For this example, we will use Jaeger running locally with docker-compose.yaml. Create the following docker-compose file in a convenient directory:

docker-compose.yaml

services:
  jaeger:
    image: jaegertracing/jaeger:latest
    container_name: jaeger
    ports:
      - "16686:16686" # Web UI
      - "4318:4318" # OTLP HTTP

    restart: unless-stopped

Run docker-compose up to download and start the server. Navigate to http://localhost:16686 to access the Jaeger UI.

Configure fast-agent

Next, update your fast-agent.yaml to enable telemetry:

fast-agent.yaml

otel:
  enabled: true
  otlp_endpoint: "http://localhost:4318/v1/traces"  # This is the default value

Then, run your agent as normal - telemetry is transmitted by default to http://localhost:4318/v1/traces. From the Jaeger UI use the "Services" drop down to select fast-agent and click "Find Traces" to view the output.

OpenAI Responses WebSocket instrumentation

With the currently pinned OpenLLMetry OpenAI instrumentation (opentelemetry-instrumentation-openai==0.62.1), Responses API calls using the WebSocket transport produce fast-agent's agent and root spans, but not the detailed openai.response provider span. To capture provider metadata such as model and response IDs, token usage, and finish reasons, use the SDK-backed SSE transport by adding transport=sse to the model string, for example responses.gpt-5.6-terra?transport=sse.

For full configuration settings, check the configuration file reference