Runnable examples

Every example below runs on scripted models by default, with no key: it exercises the workflow and its plumbing, not model quality. The workflow examples use fictional inputs. Start with the support example.

View the static diagram

Run the support example

The workflow in the animation, from a checkout (Node 24.11 or newer, and npm):

git clone --branch main --single-branch https://github.com/Parcha-ai/agentrun.git
cd agentrun
# With nvm: nvm install && nvm use
npm ci --ignore-scripts
npm run build
npm run demo:support

This runs the interpreter with scripted tools, Jev answers, and agent responses. It needs no API key and sends no customer replies. The command prints a report for each case:

Request	Agent calls	Decision calls	Result
Reset a password	0	1	Return the help answer
Find an invoice	0	1	Return the help answer
Investigate a failed payment	1	2	Return the investigation answer
Payment still unresolved	1	2	Escalate for review

npm run demo:support -- payment
npm run test:support

Try npm run demo:support -- unresolved to see an escalation. It exits with code 2. The responses are scripted; changing a prompt does not change them. Connect live Jev and your agent next.

Research demo

From the built checkout, a scripted research workflow answers "Should our team move its docs from a wiki into the code repository?":

npm run demo
npm run eval:research

It plans subquestions, researches them in parallel, screens evidence, and writes a report. The evaluation checks six labeled cases with scripted responses. Connect real Jev and Pi models.

Every example

After npm ci --ignore-scripts and npm run build:

Example	Run	What to change
Support answers	npm run demo:support	Connect your search tool and agent; check an answer before returning it.
Typed research	npm run demo	Reuse one research component across questions and stop when evidence is missing.
Rank a list	npm run demo:rank	Score candidates against your brief, drop duplicates, cap each bucket, keep the top k; call it from another workflow.
Standalone TypeScript app	Follow the app's install steps, then npm test and npm start	Build outside this checkout with public package imports.

The ranking example is a reusable workflow built from existing nodes, not a node of its own. A sift scores every candidate with a leveled question, and the continuous score in <as>.answers[i].answers.<question>.score orders them. Sorting, the minimum score, per-bucket quotas and the cut-off are a code step. A second sift over pairs finds near-duplicates among the best consider candidates, the only ones that can make the list. That costs consider × (consider − 1) / 2 questions, so the caller chooses it. A long list needs no batching in the workflow: a sift over the host's per-request limits splits itself. npm run demo:rank -- --live --input request.json ranks your own list with Jev.

Each example includes a failure or uncertainty path. npm run demo:support -- unresolved and npm run demo -- --no-evidence exit with code 2 after escalation.

To connect models, follow support integration or research integration. Your application supplies tool permissions, credentials and cancellation. Scripted answers remain fixed when you edit a prompt.