---
description: Start here to integrate Opik into your Agent Spec-based application
  for end-to-end observability, unit testing, and optimization.
headline: Agent Spec | Opik Documentation
og:description: Trace Agent Spec workflows in Opik to debug and optimize agent execution.
og:site_name: Opik Documentation
og:title: Agent Spec - Opik
title: Observability for Agent Spec with Opik
---

[Open Agent Specification](https://github.com/oracle/agent-spec) is a portable
configuration language for defining agentic systems (agents, tools, and structured workflows).

Agent Spec Tracing is an extension of Agent Spec that standardizes how agent and flow executions emit traces.
This makes it easier to analyze what happened (LLM calls, tool calls, and intermediate steps) across different runtimes and adapters.

## Account Setup

[Comet](https://www.comet.com/site?from=llm&utm_source=opik&utm_medium=colab&utm_content=agentspec&utm_campaign=opik)
provides a hosted version of the Opik platform, [simply create an account](https://www.comet.com/signup?from=llm&utm_source=opik&utm_medium=colab&utm_content=agentspec&utm_campaign=opik) and grab your API Key.

> You can also run the Opik platform locally, see the [installation guide](https://www.comet.com/docs/opik/self-host/overview/?from=llm&utm_source=opik&utm_medium=colab&utm_content=agentspec&utm_campaign=opik) for more information.

## Getting Started

### Installation

To use Agent Spec with Opik, install `opik` and `pyagentspec`:

```bash
pip install -U opik pyagentspec opentelemetry-sdk opentelemetry-instrumentation
```

If you are using the LangGraph adapter (as in the example below), install the LangGraph extra as well:

```bash
pip install -U "pyagentspec[langgraph]"
```

If you are using another framework, you can install the respective extra for `pyagentspec`, according to the
[installation instructions](https://github.com/oracle/agent-spec#installation).

### Configuring Opik

Configure the Opik Python SDK for your deployment type.
See the [Python SDK Configuration guide](/tracing/advanced/sdk_configuration) for detailed instructions on:

- **CLI configuration**: `opik configure`
- **Code configuration**: `opik.configure()`
- **Self-hosted vs Cloud vs Enterprise** setup
- **Configuration files** and environment variables

### Configuring your LLM provider

In order to run the example below, you will need to configure your LLM provider API keys. For this example, we'll use OpenAI.
You can [find or create your API keys in these pages](https://platform.openai.com/settings/organization/api-keys):

You can set them as environment variables:

```bash
export OPENAI_API_KEY="YOUR_API_KEY"
```

Or set them programmatically:

```python
import os
import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
```

## Tracing Agent Spec workflows with Opik

Opik provides an `AgentSpecInstrumentor` that connects Agent Spec Tracing to Opik.
Wrap your Agent Spec runtime execution in the instrumentor context to capture traces.

```python
import asyncio
import uuid

from pyagentspec.agent import Agent
from pyagentspec.llms import OpenAiConfig
from pyagentspec.property import FloatProperty
from pyagentspec.tools import ServerTool


def build_agentspec_agent() -> Agent:
    tools = [
        ServerTool(
            name="sum",
            description="Sum two numbers",
            inputs=[FloatProperty(title="a"), FloatProperty(title="b")],
            outputs=[FloatProperty(title="result")],
        ),
        ServerTool(
            name="subtract",
            description="Subtract two numbers",
            inputs=[FloatProperty(title="a"), FloatProperty(title="b")],
            outputs=[FloatProperty(title="result")],
        ),
    ]

    return Agent(
        name="calculator_agent",
        description="An agent that provides assistance with tool use.",
        llm_config=OpenAiConfig(name="openai-gpt-5-mini", model_id="gpt-5-mini"),
        system_prompt=(
            "You are a helpful calculator agent.\n"
            "Your duty is to compute the result of the given operation using tools, "
            "and to output the result.\n"
            "It's important that you reply with the result only.\n"
        ),
        tools=tools,
    )


async def main():
    from opik.integrations.agentspec import AgentSpecInstrumentor
    from pyagentspec.adapters.langgraph import AgentSpecLoader

    agent = build_agentspec_agent()
    tool_registry = {
        "sum": lambda a, b: a + b,
        "subtract": lambda a, b: a - b,
    }

    langgraph_agent = AgentSpecLoader(tool_registry=tool_registry).load_component(agent)

    # Each conversation turn gets its own Opik trace; thread_id groups them into a session.
    thread_id = str(uuid.uuid4())
    messages = []
    while True:
        user_input = input("USER  >>> ")
        if user_input.lower() in ["exit", "quit"]:
            break
        messages.append({"role": "user", "content": user_input})

        with AgentSpecInstrumentor().instrument_context(
            project_name="agentspec-demo",
            mask_sensitive_information=False,
            thread_id=thread_id,
        ):
            response = langgraph_agent.invoke(
                input={"messages": messages},
                config={"configurable": {"thread_id": "1"}},
            )

        agent_answer = response["messages"][-1].content.strip()
        print("AGENT >>>", agent_answer)
        messages.append({"role": "assistant", "content": agent_answer})


if __name__ == "__main__":
    asyncio.run(main())
```

<Tip>
  Agent Spec traces often include prompts, tool inputs/outputs, and messages. If you need to avoid logging sensitive
  information, set `mask_sensitive_information=True`.
</Tip>

Once you run the script and interact with your agent, you can inspect the trace tree in Opik to debug tool usage,
LLM generations, and intermediate steps.

## Further improvements

If you would like to see us improve this integration, simply open a new feature
request on [Github](https://github.com/comet-ml/opik/issues).
