Documentation

Trefur is the observability platform for AI agents. Trace every prompt, tool call, retry, and sub-agent. Built on OpenTelemetry GenAI Semantic Conventions so you can instrument once and capture everything — across runtimes, vendors, and deployment patterns.

What can I do with Trefur?

  • Trace every agent call — inputs, outputs, latency, cost, tool calls, sub-agent spawns, MCP traffic.
  • Debug failing agents — drill from a failing customer interaction down to the specific decision that went wrong, in the specific deployment, on the specific build.
  • Catch regressions before customers do — alert on p95 latency, retry-rate spikes, cost drift, and error-rate envelopes; route to Slack, PagerDuty, Teams, or any webhook.
  • Capture operational audit logs — every agent run recorded with full call graph for multi-agent topologies.

Two paths in. Pick whichever fits.

Choose the SDK when…Choose the collector when…
You own the agent code and can pip install / npm install / go get a dependency.You need to capture telemetry from agents you do not own — third-party tools, MCP servers, CLI sessions.
Single process / single language stack.Multi-process, multi-language fleet, or a developer machine.
You want auto-instrumentation for OpenAI / Anthropic / LangChain / Vercel AI / MCP.You want local batching, redaction, on-disk buffering, OTel passthrough.
Key prefix trf_obs_*.Key prefix trf_coll_*.

The two paths emit identical payloads to the same backend. Run both — many customers do.

For AI assistants reading this

Every page in this documentation site has a machine-readable mirror at <page>.md and a Schema.org TechArticle JSON-LD block embedded in the HTML. A discoverability manifest lives at /llms.txt. See the environment-variables reference for SDK + collector configuration, or the API reference for ingest and OAuth surfaces.