Stacks/Healthcare coaching
Numan's Nu AI health coach
Numan built Nu, an AI health coach, as a LangGraph supervisor with domain-specialist sub-agents and safety guard nodes on every reply. Evals run through the production graph itself: per-category fixture datasets, release-gating safety probes, and a newer simulated-conversation layer. Two frontier providers are chosen per agent, and LangSmith holds traces, datasets, and experiments.
- Use case
- An AI health coach for patients on Numan's weight-loss and long-term-condition programmes, kept inside a UK regulatory line (coaching and education, no diagnosis or prescribing)
- Category
- Healthcare coaching
- Team size
- not stated
- Orchestrator / framework
- Nu is a LangGraph graph: a supervisor model hands off to domain-specialist sub-agents, each with its own prompt and tools. Guard nodes run on every conversation; a safety guard reviews each outgoing response against fixed clinical safety rules and sends unsafe drafts back to the supervisor to regenerate.
- Memory
- not stated
- Evals
- Every eval runs through the same LangGraph graph patients use (no separate harness). Themed fixture datasets cover coaching quality, safety, medication queries, and routing, scored per category rather than as one average. Safety probes gate releases automatically. A prototype layer simulates multi-turn conversations with patient personas played by a cheaper model; clinician-owned rubrics are the goal for gating safety claims.
- Observability
- Every conversation, real or simulated, lands as a trace in LangSmith. Reviewers annotate traces, and datasets and experiments (including a side-by-side provider comparison behind a one-day migration) live there. Online evaluators on live conversations are the next step.
- Cost (stated in writeup)
- Not stated
- Cost per output (stated)
- Not stated in the writeup
- WhatWeRun estimate
WhatWeRun estimate
Not enough info to estimate
The post doesn't name the models (only 'two frontier providers'), conversation length, or volume.
Reviewed Nov 13, 2026
- Models
- Observability tools
- Other tools
- LLM-as-judge rubricsSafety guard nodes
- Source
- https://www.langchain.com/blog/how-numan-built-an-eval-first-ai-health-coach-with-langgraph-and-langsmith
Asif Laldin, Marcus Wadge-Dale, Shubhangi Hora, and Konstantinos Georgakilas · Published Oct 19, 2026 · Last verified Nov 13, 2026
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