> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Unit testing

> Test agent logic without API calls using fake chat models and in-memory persistence.

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.

## Mock chat model

LangChain provides [`GenericFakeChatModel`](https://reference.langchain.com/python/langchain-core/language_models/fake_chat_models/GenericFakeChatModel) for mocking text responses. It accepts an iterator of responses ([`AIMessage`](https://reference.langchain.com/python/langchain-core/messages/ai/AIMessage) objects or strings) and returns one per invocation. It supports both regular and streaming usage.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
model.invoke("hello, again!")
# AIMessage(content='bar', ...)
```

## InMemorySaver checkpointer

To enable persistence during testing, you can use the [`InMemorySaver`](https://reference.langchain.com/python/langgraph/checkpoints/#langgraph.checkpoint.memory.InMemorySaver) checkpointer. This allows you to simulate multiple turns to test state-dependent behavior:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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"}}
)
```

## Next steps

Learn how to test your agent with real model provider APIs in [Integration testing](/oss/python/langchain/test/integration-testing).

***

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