pip install tenro
# or
uv add tenro

# myapp/agent.py
from tenro import link_agent, link_tool

@link_tool
def search(query: str) -> list[str]:
    ...  # calls external API

@link_agent("Assistant", entry_points="run")
class AssistantAgent:
    def run(self, task: str) -> str:
        ...  # agent loop: LLM calls tools, returns final answer

# tests/test_agent.py
from tenro import Provider, ToolCall
from tenro.simulate import llm, tool
from myapp.agent import search, AssistantAgent
from tenro.testing import tenro

@tenro
def test_agent():
    tool.simulate(search, result=["Simulated Doc"])
    llm.simulate(
        Provider.ANTHROPIC,
        responses=[
            ToolCall(search, query="Find docs"),
            "Summary of docs.",
        ],
    )

    result = AssistantAgent().run("Find docs")

    assert result == "Summary of docs."
    tool.verify(search)
    llm.verify_many(Provider.ANTHROPIC, count=2)

# test_helpers.py - you write and maintain this
def mock_llm_response(content=None, tool_call=None):
    if tool_call:
        message = ChatCompletionMessage(
            role="assistant", content=None,
            tool_calls=[ChatCompletionMessageToolCall(
                id="call_abc", type="function",
                function=Function(name=tool_call["name"], arguments=json.dumps(tool_call["args"]))
            )]
        )
    else:
        message = ChatCompletionMessage(role="assistant", content=content, tool_calls=None)
    return ChatCompletion(
        id="chatcmpl-123", created=0, model="gpt-5", object="chat.completion",
        choices=[Choice(index=0, finish_reason="stop", message=message)]
    )

# test_agent.py
@patch("myapp.tools.get_weather")
@patch("openai.chat.completions.create")
def test_agent(mock_llm, mock_weather):
    mock_weather.return_value = {"temp": 72, "condition": "sunny"}
    mock_llm.side_effect = [
        mock_llm_response(tool_call={"name": "get_weather", "args": {"city": "Paris"}}),
        mock_llm_response(content="It's 72°F and sunny in Paris."),
    ]
    result = my_agent.run("Weather in Paris?")
    assert result == "It's 72°F and sunny in Paris."
    mock_weather.assert_called_once_with(city="Paris")

from tenro import Provider, ToolCall
from tenro.simulate import llm, tool
from myapp.agent import get_weather, WeatherAgent
from tenro.testing import tenro

@tenro
def test_agent():
    tool.simulate(get_weather, result={"temp": 72, "condition": "sunny"})
    llm.simulate(
        Provider.OPENAI,
        responses=[
            ToolCall(get_weather, city="Paris"),
            "It's 72°F and sunny in Paris.",
        ],
    )

    result = WeatherAgent().run("Weather in Paris?")

    tool.verify(get_weather)
    llm.verify_many(Provider.OPENAI, count=2)
    assert result == "It's 72°F and sunny in Paris."

from tenro import link_agent, link_tool

@link_tool
def search(query: str) -> list[str]:
    ...  # calls external API

@link_agent("Manager", entry_points="run")
class ManagerAgent:
    def run(self, task: str) -> str:
        ...  # LLM calls search tool, summarizes results

from tenro import Provider
from tenro.simulate import llm, tool
from tenro import ToolCall
from myapp.agent import search, MyAgent
from tenro.testing import tenro

@tenro
def test_verification():
    # Setup
    tool.simulate(search, result=["doc1", "doc2"])
    llm.simulate(
        Provider.ANTHROPIC,
        responses=[
            ToolCall(search, query="docs"),
            "Summary",
        ],
    )

    # Run
    MyAgent().run("query")

    # Verify
    tool.verify(search)                              # at least once
    tool.verify_many(search, count=1)                # exactly once
    llm.verify_many(Provider.ANTHROPIC, count=2)     # exactly twice

    # Access call data
    assert llm.calls()[1].response == "Summary"

🤖 SupportAgent
   ├─ → user: "My order #12345 hasn't arrived"
   │
   ├─ 🧠 claude-sonnet-4-5
   │     ├─ → prompt: "Help customer: My order #12345 hasn't arrived"
   │     └─ ← tool_call: lookup_order(order_id='12345')
   │
   ├─ 🔧 lookup_order
   │     ├─ → order_id='12345'
   │     └─ ← {'status': 'shipped', 'eta': '2025-01-02'}
   │
   ├─ 🧠 claude-sonnet-4-5
   │     ├─ → prompt: "Tool result: {'status': 'shipped', ...}"
   │     └─ ← "Your order has shipped and will arrive by Jan 2nd!"
   │
   └─ ← "Your order has shipped and will arrive by Jan 2nd!"

────────────────────────────────────────────────────────────────
Summary: 1 agent | 2 LLM calls | 1 tool call | Total: 1.24s