Testing agents

The vision_agents.testing module provides a lightweight testing layer for verifying agent behavior — tool calls, arguments, responses, and intent — without spinning up audio/video infrastructure.

This framework uses familiar pytest patterns. No custom test runner required.

Installation

The testing module is included with Vision Agents:

uv add vision-agents

Configure pytest for async support in pytest.ini:

[pytest]
asyncio_mode = auto

Core concepts

Component	Purpose
`TestSession`	Async context manager that wraps an LLM for testing
`TestResponse`	Result of a conversation turn with events and assertions
`LLMJudge`	Evaluates agent responses against target intents
`TestSession.mock_functions`	Wraps tools in `AsyncMock` for call tracking

Basic usage

Testing a greeting

from vision_agents.plugins import gemini
from vision_agents.testing import LLMJudge, TestSession

async def test_greeting():
    llm = gemini.LLM()
    judge = LLMJudge(gemini.LLM())

    async with TestSession(llm=llm, instructions="Be friendly") as session:
        response = await session.simple_response("Hello")

        # Verify no tools were called
        assert response.function_calls == []

        # Judge the response intent
        verdict = await judge.evaluate(
            response.chat_messages[0],
            intent="Friendly greeting"
        )
        assert verdict.success, verdict.reason

Testing tool calls

async def test_weather():
    llm = gemini.LLM()
    judge = LLMJudge(gemini.LLM())

    @llm.register_function(description="Get weather for a location")
    async def get_weather(location: str) -> dict:
        return {"temp": 72, "condition": "sunny"}

    async with TestSession(llm=llm, instructions="You can check weather") as session:
        response = await session.simple_response("Weather in Tokyo?")

        # Assert the tool was called with expected arguments
        response.assert_function_called("get_weather", arguments={"location": "Tokyo"})

        # Judge the response
        verdict = await judge.evaluate(
            response.chat_messages[0],
            intent="Reports weather for Tokyo"
        )
        assert verdict.success, verdict.reason

TestResponse assertions

TestResponse provides built-in assertion methods:

assert_function_called

Verifies a tool was called with expected arguments (partial match):

# Check function was called with specific argument
response.assert_function_called("get_weather", arguments={"location": "Tokyo"})

# Check function was called (any arguments)
response.assert_function_called("get_weather")

# Check any function was called
response.assert_function_called()

assert_function_output

Verifies tool output:

# Check exact output
response.assert_function_output("get_weather", output={"temp": 72, "condition": "sunny"})

# Check if output was an error
response.assert_function_output("get_weather", is_error=True)

Accessing events directly

# Pre-computed lists for inspection
response.function_calls  # List of FunctionCallEvent
response.chat_messages   # List of ChatMessageEvent
response.events          # All events in order
response.output          # Final assistant message text
response.duration_ms     # Response time in milliseconds

Mocking LLM functions

mock_functions

Use TestSession.mock_functions to wrap functions into AsyncMock for call tracking with standard unittest.mock assertions:

async def test_with_mock_functions():
    llm = gemini.LLM()

    @llm.register_function(description="Get weather")
    async def get_weather(location: str) -> dict:
        return {"temp": 72}

    async def fake_weather(**_) -> dict:
        return {"temp": 55, "condition": "rainy"}

    async with TestSession(llm=llm, instructions="...") as session:
        with session.mock_functions(
            {"get_weather": fake_weather}
        ) as mocked:
            response = await session.simple_response("Weather in Berlin?")

            # unittest.mock assertions
            mocked["get_weather"].assert_called_once()
            mocked["get_weather"].assert_called_with(location="Berlin")

            # TestResponse assertion
            response.assert_function_output(
                "get_weather",
                output={"temp": 55, "condition": "rainy"}
            )

LLM-as-judge

LLMJudge uses a separate LLM instance to evaluate whether agent responses match target intents:

from vision_agents.testing import LLMJudge, JudgeVerdict

# Use a separate LLM instance for judging
judge = LLMJudge(gemini.LLM())

# Evaluate a response
verdict: JudgeVerdict = await judge.evaluate(
    response.chat_messages[0],
    intent="Provides a helpful, accurate weather report"
)

if verdict.success:
    print(f"Passed: {verdict.reason}")
else:
    print(f"Failed: {verdict.reason}")

Use a separate LLM instance for the judge to avoid polluting the agent’s conversation history.

Event types

The framework captures three event types during a conversation turn:

Event	Description	Fields
`ChatMessageEvent`	Assistant or user message	`role`, `content`
`FunctionCallEvent`	Tool invocation request	`name`, `arguments`, `tool_call_id`
`FunctionCallOutputEvent`	Tool execution result	`name`, `output`, `is_error`, `execution_time_ms`

Complete example

import os
import pytest
from vision_agents.plugins import gemini
from vision_agents.testing import LLMJudge, TestSession

MODEL = "gemini-flash-lite-latest"
INSTRUCTIONS = """You are a helpful assistant.
You can check the weather using the get_weather tool."""


def setup_llm(model: str):
    llm = gemini.LLM(model)

    @llm.register_function(description="Get weather for a location")
    async def get_weather(location: str) -> dict:
        return {"temp_f": 72, "condition": "sunny"}

    return llm


@pytest.mark.integration
async def test_greeting():
    """Agent gives a friendly, short greeting."""
    llm = setup_llm(MODEL)
    judge = LLMJudge(gemini.LLM(MODEL))

    async with TestSession(llm=llm, instructions=INSTRUCTIONS) as session:
        response = await session.simple_response("Hey there!")
        assert response.function_calls == []
        verdict = await judge.evaluate(
            response.chat_messages[0],
            intent="Friendly, short greeting"
        )
        assert verdict.success, verdict.reason


@pytest.mark.integration
async def test_weather_tool_call():
    """Agent calls get_weather with the right location."""
    llm = setup_llm(MODEL)
    judge = LLMJudge(gemini.LLM(MODEL))

    async with TestSession(llm=llm, instructions=INSTRUCTIONS) as session:
        response = await session.simple_response("What's the weather in Berlin?")
        response.assert_function_called("get_weather", arguments={"location": "Berlin"})
        verdict = await judge.evaluate(
            response.chat_messages[0],
            intent="Reports current weather for Berlin"
        )
        assert verdict.success, verdict.reason


@pytest.mark.integration
async def test_weather_mocked():
    """Verify tool calls with mocked implementation."""
    llm = setup_llm(MODEL)
    judge = LLMJudge(gemini.LLM(MODEL))

    async def fake_weather(**_) -> dict:
        return {"temp_f": 55, "condition": "rainy"}

    async with TestSession(llm=llm, instructions=INSTRUCTIONS) as session:
        with session.mock_functions(
            {"get_weather": fake_weather}
        ) as mocked:
            response = await session.simple_response("Weather in Berlin?")

            mocked["get_weather"].assert_called_once()
            mocked["get_weather"].assert_called_with(location="Berlin")

            response.assert_function_output(
                "get_weather",
                output={"temp_f": 55, "condition": "rainy"}
            )

            verdict = await judge.evaluate(
                response.chat_messages[0],
                intent="Reports rainy weather for Berlin"
            )
            assert verdict.success, verdict.reason

Run tests:

uv run pytest tests/ -m integration

API reference

TestSession

Parameter	Type	Description
`llm`	`LLM`	LLM instance with tools registered
`instructions`	`str`	System instructions for the agent

Methods:

simple_response(text: str) -> TestResponse — Send user text and capture response
mock_functions(mocks: dict) -> ContextManager[dict[str, AsyncMock]] — Mock tools with call tracking

TestResponse

Property	Type	Description
`input`	`str`	User input text
`output`	`str \| None`	Final assistant message
`events`	`list[RunEvent]`	All captured events
`function_calls`	`list[FunctionCallEvent]`	Tool call events
`chat_messages`	`list[ChatMessageEvent]`	Message events
`duration_ms`	`float`	Response time

LLMJudge

Parameter	Type	Description
`llm`	`LLM`	Separate LLM instance for evaluation

Methods:

evaluate(event: ChatMessageEvent, intent: str) -> JudgeVerdict — Evaluate response against intent

JudgeVerdict

Property	Type	Description
`success`	`bool`	Whether the intent was fulfilled
`reason`	`str`	Explanation of the verdict

Next steps

MCP and function calling

Simple agent example

Build a basic agent with tools

Deploying Agents

Voice

Video

Testing

Tools & Knowledge

Installation

Core concepts

Basic usage

Testing a greeting

Testing tool calls

TestResponse assertions

assert_function_called

assert_function_output

Accessing events directly

Mocking LLM functions

mock_functions

LLM-as-judge

Event types

Complete example

API reference

TestSession

TestResponse

LLMJudge

JudgeVerdict

Next steps

MCP and function calling

Simple agent example

​Installation

​Core concepts

​Basic usage

​Testing a greeting

​Testing tool calls

​TestResponse assertions

​assert_function_called

​assert_function_output

​Accessing events directly

​Mocking LLM functions

​mock_functions

​LLM-as-judge

​Event types

​Complete example

​API reference

​TestSession

​TestResponse

​LLMJudge

​JudgeVerdict

​Next steps

MCP and function calling

Simple agent example

Installation

Core concepts

Basic usage

Testing a greeting

Testing tool calls

TestResponse assertions

assert_function_called

assert_function_output

Accessing events directly

Mocking LLM functions

mock_functions

LLM-as-judge

Event types

Complete example

API reference

TestSession

TestResponse

LLMJudge

JudgeVerdict

Next steps