Know what your RAG pipeline is actually doing
Obsyn traces every retrieval, score, and generation — so "why did it hallucinate?" becomes a five-minute investigation instead of a research project.
Free tier · no credit card · first trace in 60 seconds
Built for production RAG
Everything between the query and the answer
Full-trace retrieval
See every chunk retrieved, its embedding score, and whether the right document won the vector match.
Quality scoring
Faithfulness and relevance scored per response, trended over time, alerted when they slip.
Vector store insights
Cluster drift, dead indexes, and stale embeddings surfaced before they poison answers.
Anomaly detection
Latency spikes, context overflow, and failure clusters classified automatically — not by you.
Instrument once
Two lines. Every span.
Wrap any function — retrievers, rerankers, LLM calls, tools. Obsyn builds the full execution tree automatically: query rewrites, chunk scores, rendered prompts, token costs, everything.
- Framework-agnostic — LangChain, LlamaIndex, raw SDKs
- OpenTelemetry-native export
- Zero-config sampling and PII redaction
from obsyn import observe
@observe()
def answer(question: str):
chunks = retriever.retrieve(question)
llm.complete(prompt, chunks)Your next hallucination is already in the data
Start tracing today. Find out what your retriever is really doing before your users tell you.