OpenTelemetry

How it works

OpenTelemetry (OTel) ingestion is built directly into the Muster backend. There is no sidecar connector to deploy — your agents export traces straight to your Muster instance over HTTPS. This gives full coverage across:

Any language (Python, Go, Java, Node.js, Rust)
Any framework with OTel support (LangChain, CrewAI, LlamaIndex, custom agents)
Agents that don’t have a dedicated Muster connector

What your developers do

Set three environment variables on your agent. That’s it.

OTEL_EXPORTER_OTLP_ENDPOINT=https://backend.YOUR-INSTANCE.getmuster.io/api/v1/otel
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer <muster-jwt-token>
OTEL_EXPORTER_OTLP_PROTOCOL=http/json

Get the JWT by calling POST /auth/login with a service account user, or copy it from your Muster dashboard → Settings → API Access. No code changes are required if your agent already emits OTel spans.

Adding OTel to a Python agent

If your agent doesn’t yet emit OTel spans, add instrumentation once at startup:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
import os

# Configure once at startup
provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(OTLPSpanExporter(
        endpoint=os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] + "/v1/traces",
        headers={"Authorization": f"Bearer {os.environ['MUSTER_JWT']}"},
    ))
)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("my-agent", service_name="invoice-processor")

# Instrument your agent runs
def run_agent(input: str) -> str:
    with tracer.start_as_current_span("agent.run") as span:
        span.set_attribute("service.name", "invoice-processor")
        result = llm.invoke(input)
        span.set_attribute("gen_ai.usage.input_tokens", result.usage.prompt_tokens)
        span.set_attribute("gen_ai.usage.output_tokens", result.usage.completion_tokens)
        span.set_attribute("gen_ai.response.model", "gpt-4o")
        return result.content

The service.name attribute becomes the agent name in Muster. New service names are auto-added to your Discovery queue for review.

LangChain — zero-code instrumentation

If you use LangChain, one line enables automatic tracing of all chains and agents:

from opentelemetry.instrumentation.langchain import LangchainInstrumentor
LangchainInstrumentor().instrument()
# All chains, agents, and LLM calls now emit OTel spans automatically

Then set the three env vars above and Muster receives full trace data immediately.

Check connection status

GET /api/v1/otel/status
Authorization: Bearer <token>

Returns current connector status, executions received in the last 24 hours, and the exact env vars to use for your instance.

What Muster does automatically

Once traces arrive:

Action	Details
Agent discovery	New `service.name` values appear in Discovery as UNREVIEWED
Execution recording	Each agent span stored as a connector execution
Cost tracking	Token usage attributes → cost events (for approved agents)
Quality inference	Check inference runs server-side on each execution

Supported semantic conventions

Attribute	Description
`service.name`	Agent name (required)
`gen_ai.usage.input_tokens`	Prompt token count (GenAI convention)
`gen_ai.usage.output_tokens`	Completion token count (GenAI convention)
`gen_ai.response.model`	Model name
`gen_ai.request.model`	Requested model name
`llm.usage.prompt_tokens`	Prompt tokens (LangChain format)
`llm.usage.completion_tokens`	Completion tokens (LangChain format)
`llm.model_name`	Model name (LangChain format)

Supported agent span names

Muster identifies root agent executions from these span names (partial match): agent.run, agent.execute, agent.invoke, langchain.chain, langchain.agent, crewai.task, crewai.agent, openai.chat, anthropic.messages, bedrock.invoke, llm.completion, chain.run, tool.run To add custom span names, contact Elitery.

Why OTel instead of a platform connector?

	OTel	Platform connector
Code changes	3 env vars	None
Framework coverage	Universal	Per-platform only
Data richness	Full traces + spans	API metadata only
Latency	Real-time	Polling (minutes)
Works on no-code tools (n8n, Flowise)	No	Yes

Use OTel for custom-coded agents. Use platform connectors for no-code/low-code tools where you can’t set env vars.

Getting Started

Integration

Integrations

Connectors

Concepts

How it works

What your developers do

Adding OTel to a Python agent

LangChain — zero-code instrumentation

Check connection status

What Muster does automatically

Supported semantic conventions

Supported agent span names

Why OTel instead of a platform connector?

​How it works

​What your developers do

​Adding OTel to a Python agent

​LangChain — zero-code instrumentation

​Check connection status

​What Muster does automatically

​Supported semantic conventions

​Supported agent span names

​Why OTel instead of a platform connector?

How it works

What your developers do

Adding OTel to a Python agent

LangChain — zero-code instrumentation

Check connection status

What Muster does automatically

Supported semantic conventions

Supported agent span names

Why OTel instead of a platform connector?