Documentation Index
Fetch the complete documentation index at: https://docs.getmuster.io/llms.txt
Use this file to discover all available pages before exploring further.
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
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
Check connection status
GET /api/v1/otel/status
Authorization: Bearer <token>
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.
| 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 |
