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Guides are end-to-end walkthroughs. Where the rest of the docs explain one feature at a time, a guide takes you from an empty project to a finished outcome, stitching tracing, metrics, Halo, and your coding agent into one continuous loop.

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Start here

Optimize an agent end to end

Install tracing, record real traces, read the metrics, run Halo to find what to fix, then apply the fixes by hand or by pulling them straight into your coding agent. The full Halo loop, start to finish.

Try HALO with a demo repo

Skip instrumentation. Clone a ready-to-run search agent, seed real traces, run Halo to find what’s wrong, and let your coding agent apply the fix through the MCP server. The full loop in about fifteen minutes.

Run HALO on your machine with the desktop app

The same loop, fully local. Stream traces from a demo agent into the free HALO desktop app, run Halo with your own model provider, and apply the fixes in your editor. No account, no cloud.

Measure your agent's quality with Signals

Turn raw traces into metrics. Define plain-language classifiers — frustration, sentiment, task outcome — and let an LLM judge label every span automatically, then filter and chart your traces by them.

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What you need

Every guide assumes you have:
  • A free Inference account.
  • An app or agent that makes LLM calls (any provider or framework).
  • A terminal with Node.js or Python, depending on your stack.
More guides are on the way. If there’s a workflow you want walked through end to end, let us know.