How to Test LangGraph Agents With TestMu AI

LangGraph agents are text agents exposed over HTTP, with state held in a graph and threaded across turns. There is no voice or phone surface. TestMu AI connects to the deployed endpoint, drives full multi-turn conversations across personas and edge cases, and scores every run.

Before You Begin

Before you connect a LangGraph agent, make sure you have:

A deployed graph with a reachable endpoint, self-hosted or on LangGraph Platform
TestMu AI workspace with agent-testing permissions
Source docs for scenario generation: prompt, PRD, or knowledge base

Test a LangGraph Chat Agent

Chat testing covers text conversations against a deployed graph, whether streaming or non-streaming. Upload the graph definition and supporting docs to generate scenarios, then create the endpoint profile using the deployment's REST endpoint, with a fresh thread per scenario. It scores reasoning, tool call correctness, grounding, and safety, and runs in CI on every commit.

Full setup: Chat agent testing

What You Get With Agent Testing

Every LangGraph run, on any surface it supports, is scored across:

30+ metrics across 8 categories, with configurable thresholds
Context-aware scenario generation
Adversarial testing and automated issue detection
Multilingual conversations
Quality scoring
Automated issue detection

LangGraph-Specific Considerations

A few LangGraph behaviours are worth building dedicated scenarios around:

State lives on the thread, so reusing one across scenarios contaminates results
Human-in-the-loop interrupts pause the graph mid-run, and a harness that does not resume reads the pause as a hang
Tool calls and their failure branches are where these agents actually break, so weight scenarios there
Deployment varies across self-hosted, LangGraph Platform, and custom servers, which changes the endpoint shape
There is no voice or phone surface, so voice metrics and personas do not apply