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 agent data gets into Muster
There are three integration paths. Most customers use OTel and platform connectors for full coverage from day one, then add the SDK to their most critical agents.
| OTel | Platform connectors | SDK |
|---|
| Best for | Custom-coded agents | No-code/low-code tools | Critical agents needing precise checks |
| Setup | 3 env vars | Elitery deploys | Developer adds @agent.govern() |
| Quality signals | ✅ Inferred by Muster | ✅ Inferred by Muster | ✅ Exact — you define the rules |
| Token / cost tracking | ✅ | ✅ | ✅ |
| Discovery | ✅ | ✅ | ❌ |
Layer 1 — OTel (primary for coded agents)
For any agent you can configure, OTel is the recommended path. Set three env vars and Muster receives traces, infers quality checks, and tracks costs automatically.
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
For platforms where you can’t set env vars — visual workflow tools and managed AI services. Elitery deploys and manages all connectors. Same check inference engine applies.
| Connector | What it covers | How it discovers |
|---|
| n8n | n8n workflows with LLM nodes | Workflow API scan |
| AWS Bedrock | Bedrock agents | list_agents() API |
| LangChain / LangSmith | LangChain agents | LangSmith project scan |
| Langfuse | Any agent traced to Langfuse | Trace scan |
| Azure AI | Azure OpenAI + AI Foundry | ARM API scan |
Layer 3 — SDK (for critical agents)
The SDK adds precise, business-rule checks on top of OTel/connector coverage. Use it for agents where inferred checks aren’t enough — financial calculations, compliance decisions, external writes.
@agent.govern(
required_fields=["vendor_name", "invoice_number", "total"],
arithmetic=[{"check_id": "total_check", "result_field": "total",
"component_fields": ["subtotal", "tax_amount"], "operation": "sum"}],
)
async def process_invoice(input_data: dict) -> dict:
return extracted_output # your existing code, unchanged
Check inference — how inferred signals work
When OTel or a connector submits an execution, Muster infers quality checks automatically in four layers:
- Name matching — agent name → check category (financial, legal, risk, support)
- Schema analysis — output field names → check types (numeric, decision, required fields)
- Historical patterns — after 50+ executions, distribution-based checks
- LLM fallback — Claude generates check suggestions from agent name + output structure
Inferred checks are good enough for monitoring and trend detection. For agents where you need exact validation with specific expected values — add the SDK.
Recommended approach by agent type
| Agent | Recommended path |
|---|
| Custom Python/Go/Node agent | OTel |
| LangChain, CrewAI, LlamaIndex | OTel (native support) |
| n8n, Flowise, Dify workflow | Platform connector |
| AWS Bedrock, Azure AI Foundry | Platform connector |
| Invoice / AP processing agent | OTel or connector + SDK |
| Compliance / legal review agent | OTel or connector + SDK |
| Fraud / risk screening agent | OTel or connector + SDK |
