Catch silentagent failuresbefore productionCatch silent agent failuresbefore production
Simulate LLM responses, messy tool outputs, and failure cases so you can verify what your agent did, not just what it returned.
pip install tenro@tenro.simulate
def test_cancel_order_simulation():
llm.simulate(
Provider.ANTHROPIC,
responses=[
ToolCall(cancel_order, order_id="123"),
"Done! Order #123 has been cancelled.",
],
)
tool.simulate(cancel_order, result={"status": "cancelled"})
support_agent.run("Cancel my order #123")
tool.verify(cancel_order, order_id="123")
agent.verify(support_agent, result="Done! Order #123 has been cancelled.")Why simulation
Catch failures normal tests miss
An agent run can look fine while the agent used the wrong tool, trusted bad context, or chose the wrong action.
Tools use the wrong details
The call looks fine, but the agent picked the wrong customer, date range, amount, or action.
Answers hide bad decisions
The final response sounds right, but the agent took steps you would never want in production.
Demos skip messy cases
Clean demos pass. Real agent runs hit empty results, delays, bad data, and partial answers.
Loops burn through tokens
The agent retries, replans, and fills context without getting closer to the task.
How it works
Link. Simulate. Verify.
Turn the failure modes you worry about into repeatable agent tests.
Link
@link_agent | @link_toolMark the agents, tools, and LLM calls you want to test.
Simulate
llm.simulate(...)Control LLM responses, tool outputs, timeouts, empty results, and malformed payloads.
Verify
agent.verify(...)Assert the final result, tool calls, arguments, call order, and number of attempts.
Test the shape of the run
For agent tests, the final answer is not enough. Tenro helps you check which tools were called, what arguments were passed, how many times the agent retried, and whether it handled bad data correctly.
Integrations
No provider lock-in
Simulate behavior around the providers and frameworks you already use.
LLM Providers
Agent Frameworks
Early access
Test agents with repeatable simulations
Get release notes, examples, and practical testing patterns for agent teams.