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By the end of this page you’ll have the engine running locally, the dashboard connected, and a real trace from your own code on screen.
1

Install and start the engine

Every path downloads a prebuilt release - no Node, Go, or Bun needed. The startup log prints the line you’ll need next:
2

Connect the dashboard

--dev opens http://localhost:4700 in your browser. The first visit shows a connect screen - paste the Default project API key from the startup log and you’re in. The key is stored in that browser only; the sidebar’s Disconnect button forgets it.
3

Trace your agent

Point the SDK at the engine with the same key:
Then wrap your agent - a decorator for the simple case, a context manager for full control:
Using LangChain, OpenAI Agents, CrewAI, or another framework? A one-line integration captures the full execution tree automatically - see Framework Integrations.
4

See it in Observe

Open Observe → Live Traces. Your trace appears within seconds - click it for the full detail: input/output, latency, tokens, estimated cost, and (for multi-step agents) the Execution Timeline and Graph views of every step.
PLACEHOLDER: screenshot of the trace detail dialog showing the stat strip (duration, tokens, cost), the Trajectory metrics row, and the Execution Timeline with an agent span, tool span, and LLM span nested under the root.

A traced LangGraph run: graph nodes, LLM calls, and tool calls as one tree.

Where to go next

Full tracing guide

Tool calls, sessions, span trees, async agents

Evaluate with a dataset

Score your agent with an LLM judge in one script

Monitor production

Scorers and signals on live traffic

Gate releases in CI

Block merges when quality drops
Provider keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, or GEMINI_API_KEY) unlock the LLM-judge features - evaluation scoring, online evaluators, semantic patterns. Set them as environment variables on the engine or later from the dashboard’s Platform Settings. Tracing itself needs none.