Test a LangGraph agent’s graph behavior with a known starting state, a predictable model response, and controlled tool results. This lets you check whether the graph routes and updates state as intended without relying on a live model or triggering real external actions. It does not prove that a live model will make the same decisions or that an external tool will work.
Decide what the test should prove
LangGraph is a low-level framework for building stateful workflows and agents. Its graphs can combine deterministic, hand-coded steps with agentic steps, so a test can focus on the parts of the workflow your application controls. The LangGraph overview demonstrates a graph with a mock LLM node, compilation, and invocation using a fixed user message.
Before writing a test, identify the behavior you want to verify and what the graph exposes to observe it:
- Starting state: Does the graph receive the expected input?
- Routing: Given a controlled result, does execution take the intended branch?
- State updates: Are expected values or messages present after execution?
- Tool handling: Does the graph handle a supplied tool result and continue appropriately?
- Final state: Does invocation return the state your application expects?
Keep these assertions separate from claims about agent decision quality. A test that supplies a predictable model response checks how the graph handles that response; it does not measure whether a live model would choose it.
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Build a controlled graph test
Fix the initial input
Use a small graph containing only the nodes and edges relevant to the behavior under test. Pass a known input object or message to invoke, following the graph’s state schema. Fixed inputs make it easier to attribute an unexpected result to graph logic rather than to changing test data.
Control the model response
When testing orchestration, substitute a predictable model response for a live model call. This is a test-design recommendation based on the overview’s mock-LLM example: controlling the response makes it possible to repeat the same path and inspect how the graph handles it.
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Keep the test’s claim narrow. If the mocked response contains a particular answer or tool call, the test establishes how the graph processes that response—not that a production model will reliably generate it.
Supply controlled tool results
For a test that concerns what happens after a tool call, provide a controlled result at the graph boundary instead of performing the external action. LangGraph’s human-in-the-loop guide describes adding a mock tool result as messages in graph state and continuing after tool-call review or modification. This pattern can exercise result handling without making the test perform the real action.
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Use a result shaped like the state your graph expects, and verify the next state or route that follows. A mocked result can expose errors in graph handling, but cannot establish that a real service accepts the request or returns the same schema.
Assert only observable behavior
After invocation, assert the final state, relevant messages, selected route, or whether an execution path was taken—but only when the implementation exposes those values. Choose assertions that correspond directly to the behavior the test is meant to protect. For example, a test may check that a supplied tool result appears in state and that the graph reaches its expected next step.
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There is no universal assertion API implied by these examples. The official documentation demonstrates graph execution and tool-call handling, but does not provide a complete current pytest fixture-and-patching recipe. Use the APIs and state types supported by the LangGraph and related package versions installed in your project rather than treating illustrative snippets as version-independent runnable code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose between controlled tests and live integration tests
These test levels answer different questions; neither replaces the other when you need confidence in both graph behavior and external dependencies.
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| Test level | Determinism | Cost and speed | External dependencies | Failures it can expose |
|---|---|---|---|---|
| Controlled graph test | High when inputs, model responses, and tool results are fixed | No quantitative comparison is established by the cited documentation; avoiding live calls can reduce reliance on external services | Mocks can remove model and tool service calls from the tested path | Unexpected routing, state updates, or handling of the supplied response and tool result |
| Live integration test | Depends on live model and service behavior | No quantitative comparison is established by the cited documentation | Requires the live dependencies used by the test | Credential, network, service-schema, and live model behavior problems that mocks cannot verify |
Keep any live integration coverage separately identified. Run it when the properties you care about depend on actual credentials, network access, external service behavior, or live model output. The controlled test remains useful for repeatable checks of graph behavior, while the integration test addresses failures hidden by the mocks.
What a deterministic test can and cannot establish
A controlled test provides evidence about the specific path represented by its starting state and mocks: whether the graph executes, how it updates state, and how it responds to the supplied model or tool output. It does not establish the quality or consistency of live model decisions, the correctness of real tool results, or the availability of external services. Treat those as separate claims requiring live coverage where they matter.
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