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Workshop Tour

The Workshop is Loom's web UI for building, testing, and evaluating workers. It runs locally — no NATS or cloud services needed. Start it with:

loom workshop
# or: loom workshop --port 8080

Open http://localhost:8080 in your browser. Here's what each screen does.

Workers List

What you see: A table of all worker configs found in your configs/workers/ directory — both the six shipped workers and any you've created.

What you can do:

  • Click Test to open the test bench for that worker
  • Click Eval to run a test suite against a worker
  • Click a worker name to view and edit its YAML config

When to use it: This is your home screen. Come here to pick a worker to test, or to see what's available.

Test Bench

What you see: An input form for the selected worker. The fields match the worker's input schema — for the summarizer, you'll see a text box; for the classifier, you'll see text plus a categories field.

What you can do:

  • Fill in the input fields and click Run
  • See the structured JSON output, raw LLM response, token usage, and latency
  • Change the model tier (local / standard / frontier) to compare outputs
  • Iterate: edit the input, run again, compare

When to use it: This is the fastest way to try a worker. Paste any text, click Run, see what you get. No YAML editing, no terminal commands.

Tip: The test bench validates your input against the worker's schema before sending it to the LLM, and validates the output when it comes back. If either fails, you'll see exactly which fields are wrong — this is the same validation that runs in production.

Worker Detail / YAML Editor

What you see: The full YAML config for a worker, editable in the browser. Below it: version history (every save is tracked) and a clone form.

What you can do:

  • Edit the system prompt, I/O schemas, model tier, or timeout
  • Save to create a new version (the previous version is preserved)
  • Clone a worker as a starting point for a new one
  • View the diff between versions

When to use it: When you want to tweak a prompt, tighten an output schema, or create a variation of an existing worker. Edit → save → test → repeat.

Eval Runner

What you see: A form where you define test cases: each test case has an input payload and (optionally) an expected output.

What you can do:

  • Define a test suite with multiple input/expected-output pairs
  • Choose a scoring method:
  • Field match — checks whether specific fields in the output match expected values
  • Exact match — full output must match exactly
  • LLM judge — a separate model scores the output on correctness, completeness, and format
  • Run the suite and see per-case scores
  • Set a golden dataset baseline for regression detection — future runs are compared against it

When to use it: When you need to know whether a prompt change made things better or worse. Run the eval before and after, compare scores.

Eval Detail

What you see: Results for a completed eval run. Each test case shows: pass/fail, the score, the actual output, and (for LLM judge) the judge's reasoning.

What you can do:

  • Expand each case to see full input, expected output, actual output, and scoring details
  • Compare against the golden baseline (if one is set)

When to use it: After running an eval, to understand which cases passed, which failed, and why.

Pipeline List

What you see: A table of pipeline configs found in configs/orchestrators/.

What you can do:

  • Click a pipeline to open the editor

Pipeline Editor

What you see: A visual dependency graph of the pipeline stages, plus forms for editing stages.

What you can do:

  • See which stages depend on which (the graph shows parallelism)
  • Add, remove, reorder, or swap stages
  • Edit input mappings for each stage (how data flows between stages)
  • Validate the pipeline graph (catches circular dependencies, missing inputs)

When to use it: When you're building or modifying a multi-step workflow. The visual graph makes it clear what runs in parallel and what's sequential.

RAG Dashboard

What you see: An overview of your vector store — document count, embedding dimensions, disk usage, and a list of ingested channels.

What you can do:

  • See store statistics at a glance
  • Browse channels with trust and bias metadata
  • Run semantic searches directly from the browser

When to use it: After ingesting data with loom rag ingest, to explore what's in the store and run searches.

The RAG dashboard appears when you start Workshop with RAG configured (either via loom rag serve or loom workshop after loom setup has set RAG paths).

Apps

What you see: A list of deployed app bundles — self-contained packages that include worker configs, pipeline configs, and data files.

What you can do:

  • Upload a ZIP app bundle
  • View the app manifest (what workers and pipelines it contains)
  • Remove deployed apps

When to use it: When you receive a pre-packaged Loom application from someone else and want to deploy it locally.

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