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Tenro

Tenro is a modern simulation framework for testing AI agents. Simulate multi-agent workflows and tool usage without burning tokens.

  • No API costs — Tests run offline (no LLM calls)
  • Deterministic — Simulate responses, errors, and tool results
  • Workflow verification — Check tools, edge cases, and agent behaviours

How it works

Tenro intercepts LLM calls at the HTTP level. This works with any framework that uses a supported provider SDK. No patches, no special configuration.

from tenro import Provider
from tenro.simulate import llm, tool
from tenro.testing import tenro

@tenro
def test_my_agent():
    # Simulate LLM response
    llm.simulate(Provider.OPENAI, response="Hello!")

    # Run your agent - LangChain, CrewAI, OpenAI SDK, anything
    my_agent.run("query")

    # Verify behavior
    llm.verify()

Learn how it works

Choose your framework

Write tests that read like specs

No patch decorators. No response builders. Just simulate and verify.

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

@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."

Manual mocks, helper functions, boilerplate:

# test_helpers.py - you write and maintain this for each LLM provider
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")

Provider support

Provider Status
OpenAI Supported
Anthropic Supported
Google Gemini Supported

Get started

Installation Quick start