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Unit tests exercise small, deterministic pieces of your agent in isolation. By replacing the real LLM with an in-memory fake (AKA fixture), you can script exact responses (text, tool calls, and errors) so tests are fast, free, and repeatable without API keys.

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Mock chat model

LangChain provides GenericFakeChatModel for mocking text responses. It accepts an iterator of responses (AIMessage objects or strings) and returns one per invocation. It supports both regular and streaming usage. 
from langchain_core.language_models.fake_chat_models import GenericFakeChatModel

model = GenericFakeChatModel(messages=iter([
    AIMessage(content="", tool_calls=[ToolCall(name="foo", args={"bar": "baz"}, id="call_1")]),
    "bar"
]))

model.invoke("hello")
# AIMessage(content='', ..., tool_calls=[{'name': 'foo', 'args': {'bar': 'baz'}, 'id': 'call_1', 'type': 'tool_call'}])
If we invoke the model again, it will return the next item in the iterator: 
model.invoke("hello, again!")
# AIMessage(content='bar', ...)

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InMemorySaver checkpointer

To enable persistence during testing, you can use the InMemorySaver checkpointer. This allows you to simulate multiple turns to test state-dependent behavior: 
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model,
    tools=[],
    checkpointer=InMemorySaver()
)

# First invocation
agent.invoke(
    {"messages": [HumanMessage(content="I live in Sydney, Australia")]},
    config={"configurable": {"thread_id": "session-1"}}
)

# Second invocation: the first message is persisted (Sydney location), so the model returns GMT+10 time
agent.invoke(
    {"messages": [HumanMessage(content="What's my local time?")]},
    config={"configurable": {"thread_id": "session-1"}}
)

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Next steps

Learn how to test your agent with real model provider APIs in Integration testing.
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