✨ Features

• Simulate scenarios — Control LLM responses, errors, and tool results
• Verify workflows — Check tool usage, edge cases, and agent behavior
• Run evaluations — Measure agent behavior across test cases
• Agnostic by design — Works across multiple LLM providers and agent frameworks
• Multi-agent support — Test multi-agent workflows

Install

pip install tenro

Quick Start

1. Decorate your tools and agent

Decorate with @link_tool and @link_agent.

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


def add_footer(text: str) -> str:
    # Post-processing before returning the final reply
    return f"{text}\n\nReply in the support portal for faster help."


@link_tool
def lookup_ticket(ticket_id: str) -> dict:
    return ticket_api.get(ticket_id)


@link_agent
class SupportAgent:
    def run(self, task: str) -> str:
        ...  # LLM calls tools and drafts the reply
        return add_footer(llm_response)

2. Simulate your test scenarios

Use the @tenro.simulate pytest decorator to simulate LLM calls and tool results.

# tests/test_agent.py
import tenro
from tenro import Provider, ToolCall
from tenro.simulate import llm, tool
from myapp.agent import lookup_ticket, SupportAgent


@tenro.simulate
def test_agent():
    # Simulate the tool result
    tool.simulate(
        lookup_ticket,
        result={"status": "shipped", "eta": "Apr 18"}
    )

    # Simulate the LLM calls
    llm.simulate(
        Provider.OPENAI,
        responses=[
            # 1) call the tool
            ToolCall(lookup_ticket, ticket_id="T-123"),

            # 2) generate the reply
            "Your replacement has shipped and should arrive by Apr 18."
        ]
    )

    # Run your agent
    SupportAgent().run("What's the status of ticket T-123?")

    # Verify the tool was called
    tool.verify(lookup_ticket, ticket_id="T-123")

    # Verify what the user sees and the footer got added
    agent.verify(
        SupportAgent,
        result="Your replacement has shipped and should arrive by Apr 18.\n\n"
               "Reply in the support portal for faster help."
    )

Trace Output

Tenro can print a local trace of your agent run so you can see each step of execution, including user input, LLM calls, tool calls, and the final output.

🤖 SupportAgent
   ├─ → user: "What's the status of ticket T-123?"
   │
   ├─ 🧠 GPT-5.4 [SIM]
   │     ├─ → prompt: "What's the status of ticket T-123?"
   │     └─ ← tool_call: lookup_ticket(ticket_id='T-123')
   │
   ├─ 🔧 lookup_ticket [SIM]
   │     ├─ → ticket_id='T-123'
   │     └─ ← {'status': 'shipped', 'eta': 'Apr 18'}
   │
   ├─ 🧠 GPT-5.4
   │     ├─ → prompt: "Tool result: {'status': 'shipped', 'eta': 'Apr 18'}"
   │     └─ ← "Your replacement has shipped and should arrive by Apr 18."
   │
   └─ ← "Your replacement has shipped and should arrive by Apr 18.

         Reply in the support portal for faster help."

────────────────────────────────────────────────────────────────
Summary: 1 agent | 2 LLM calls | 1 tool call | Total: 355ms

Enable trace output locally:

TENRO_PRINT_TRACE=1

How It Works

Decorate your tools, agents, and model calls with @link_tool, @link_agent, and @link_llm, define the behavior you want with Tenro’s simulation API, then run your tests normally.

Tenro helps you simulate and validate AI agents in two ways:

1. Test the agent code

Simulate LLM responses and verify that your code handles failures, retries, guardrails, and edge cases correctly.

• Invalid tool inputs
• Repeated or unexpected tool calls
• Retry, fallback, and escalation paths

2. Test the LLM behavior

Simulate tools and environment conditions to see how the model behaves under realistic scenarios.

• Tool choice and sequencing
• Incomplete or invalid tool results
• Ambiguous or inconsistent environments

LLM Provider Support

Agent Framework Support

Support

If Tenro helps you, consider starring the repo to bookmark it and help others discover Tenro.

• Report bugs — include exact steps and logs if possible
• Request features — share the use case and expected behavior
• Ask questions — usage, roadmap, or design decisions

Please use GitHub Issues for bug reports, feature requests, and questions.

Contributing

Thanks for your interest in contributing.

Tenro is evolving quickly, and the current focus is on stabilizing the core API. As a result, pull requests are not being accepted at this time.

License

Apache 2.0

Contact

• Email: support@tenro.ai