MockChatGenerator

A Chat Generator that returns predefined responses without calling any API, for tests and quick prototypes.

Most common position in a pipeline	In place of a real Chat Generator, in tests and prototypes
Mandatory init variables	None
Mandatory run variables	messages: A list of ChatMessage objects
Output variables	replies: A list of generated ChatMessage objects
API reference	Generators
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/chat/mock.py
Package name	haystack-ai

Overview

MockChatGenerator is a deterministic, zero-cost drop-in replacement for real Chat Generators such as OpenAIChatGenerator. It implements run, run_async, streaming callbacks, and serialization but never contacts an external service, which makes it ideal for unit tests, smoke tests, and quick prototypes.

The response is selected based on how the component is configured:

• Fixed response: Pass a single string or ChatMessage via responses. The same reply is returned on every call. A ChatMessage passed as a response must have the assistant role.
• Cycling responses: Pass a list of strings and/or ChatMessage objects via responses. Each call returns the next item, wrapping around to the start once the list is exhausted. This is useful to drive multi-step flows such as Agents, where the first call returns a tool call and a later call returns the final answer.
• Dynamic response: Pass a response_fn callable that receives the input messages and returns the reply as a string or an assistant ChatMessage. Use this when the reply should depend on the input. To support serialization, pass a named function.
• Echo (default): With no configuration, the component echoes back the text of the last message that has text content, so it is usable out of the box.

responses and response_fn are mutually exclusive.

Further optional parameters:

• model: The model name reported in the response metadata. Defaults to "mock-model".
• meta: Additional metadata merged into the meta of every returned ChatMessage. A per-response ChatMessage's own metadata takes precedence.
• streaming_callback: An optional callback invoked with StreamingChunk objects reconstructed from the predefined response. It lets the mock exercise streaming code paths without a real model.

Usage

On its own

python

from haystack.components.generators.chat import MockChatGenerator
from haystack.dataclasses import ChatMessage

# Fixed response
generator = MockChatGenerator(responses="Hello, this is a mock response.")
result = generator.run([ChatMessage.from_user("Hi!")])
print(result["replies"][0].text)  # "Hello, this is a mock response."

# Echo mode (default): returns the last message with text content
generator = MockChatGenerator()
result = generator.run([ChatMessage.from_user("Repeat after me")])
print(result["replies"][0].text)  # "Repeat after me"

Driving an Agent

Pass ChatMessage objects (rather than plain strings) to return tool calls or reasoning content. With cycling responses, you can script a full agent loop without a real model:

python

from haystack.components.agents import Agent
from haystack.components.generators.chat import MockChatGenerator
from haystack.dataclasses import ChatMessage, ToolCall
from haystack.tools import tool


@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Results for: {query}"


generator = MockChatGenerator(
    responses=[
        ChatMessage.from_assistant(
            tool_calls=[ToolCall(tool_name="search", arguments={"query": "Haystack"})],
        ),
        "Here is the final answer.",
    ],
)

agent = Agent(chat_generator=generator, tools=[search])
result = agent.run(messages=[ChatMessage.from_user("Tell me about Haystack")])
print(result["last_message"].text)  # "Here is the final answer."

Input-dependent responses

python

from haystack.components.generators.chat import MockChatGenerator
from haystack.dataclasses import ChatMessage


def shout_back(messages: list[ChatMessage]) -> str:
    return messages[-1].text.upper()


generator = MockChatGenerator(response_fn=shout_back)
result = generator.run([ChatMessage.from_user("hello")])
print(result["replies"][0].text)  # "HELLO"