Instrument AI Agents

import sentry_sdk

sentry_sdk.init(...)

# some example agent implementation for demonstration
my_agent = MyAgent(
    name="Weather Agent",
    model_provider="openai",
    model="o3-mini",
)

with sentry_sdk.start_span(
    op="gen_ai.invoke_agent",
    name=f"invoke_agent {my_agent.name}",
) as span:
    # set data about LLM and agent
    span.set_data("gen_ai.operation.name", "invoke_agent")
    span.set_data("gen_ai.system", my_agent.model_provider)
    span.set_data("gen_ai.request.model", my_agent.model)
    span.set_data("gen_ai.agent.name", my_agent.name)

    # run the agent
    result = my_agent.run()

    # set agent response
    # we assume result.output is a dictionary
    # type of `gen_ai.response.text` needs to be a string
    span.set_data("gen_ai.response.text", json.dumps([result.output]))

    # set token usage
    # we assume the result includes the tokens used
    span.set_data("gen_ai.usage.input_tokens", result.usage.input_tokens)
    span.set_data("gen_ai.usage.output_tokens", result.usage.output_tokens)

import sentry_sdk

sentry_sdk.init(...)

# some example implementation for demonstration
my_ai = MyAI(
    model_provider="openai",
    model="o3-mini",
    model_config={
        "temperature": 0.1,
        "presence_penalty": 0.5,
    }
)

with sentry_sdk.start_span(
    op="gen_ai.chat",
    name=f"chat {my_ai.model}",
) as span:
    # set data about LLM
    span.set_data("gen_ai.operation.name", "chat")
    span.set_data("gen_ai.system", my_ai.model_provider)
    span.set_data("gen_ai.request.model", my_ai.model)

    # set up messages for LLM
    max_tokens = 1024
    prompt = "Tell me a joke"
    messages = [
        {"role": "user", "content": prompt},
    ]

    # set chat request data
    span.set_data("gen_ai.request.message", json.dumbs(messages))
    span.set_data("gen_ai.request.max_tokens", max_tokens)
    span.set_data(
        "gen_ai.request.temperature",
        my_ai.model_config.get("temperature"),
    )
    span.set_data(
        "gen_ai.request.presence_penalty",
        my_ai.model_config.get("presence_penalty"),
    )

    # ask the LLM
    result = my_ai.messages.create(
        messages=messages,
        max_tokens=max_tokens,
    )

    # set response
    # we assume result.output is a dictionary
    # type of `gen_ai.response.text` needs to be a string
    span.set_data("gen_ai.response.text", json.dumps([result.output]))

    # set token usage
    # we assume the result includes the tokens used
    span.set_data("gen_ai.usage.input_tokens", result.usage.input_tokens)
    span.set_data("gen_ai.usage.output_tokens", result.usage.output_tokens)

import sentry_sdk

sentry_sdk.init(...)

# some example implementation for demonstration
my_ai = MyAI(
    model_provider="openai",
    model="o3-mini",
)

with sentry_sdk.start_span(op="gen_ai.chat", ...):
    #  .. some code ..
    result = my_ai.messages.create(
        messages=messages,
        max_tokens=1024,
    )
    # .. some code ..

# we assume the llm tells us to call a tool in the result
if my_should_call_tool(result):
    # we parse the result to know which tool to call
    tool = my_get_tool_to_call(result)

    with sentry_sdk.start_span(
        op="gen_ai.execute_tool",
        name=f"gen_ai.execute_tool {tool.name}"
    ) as span:
        # set data about LLM and tool
        span.set_data("gen_ai.system", my_ai.model_provider)
        span.set_data("gen_ai.request.model", my_ai.model)
        span.set_data("gen_ai.tool.type", "function")
        span.set_data("gen_ai.tool.name", tool.name)
        span.set_data("gen_ai.tool.description", tool.description)

        # run tool
        tool_result = my_run_tool(tool)

        # set tool result
        span.set_data("gen_ai.tool.output", json.dumps(tool_result))

import sentry_sdk

sentry_sdk.init(...)

# some example agent implementation for demonstration
my_agent = MyAgent(
    name="Weather Agent",
    model_provider="openai",
    model="o3-mini",
)

with sentry_sdk.start_span(op="gen_ai.invoke_agent", ...):
    #  .. some code ..
    result = my_agent.run()
    #  .. some code ..


# we assume the llm tells us to handoff to another agent
if my_should_handoff(result):
    # we parse the result to know which agent to handoff to
    other_agent = my_get_agent(result)

    with sentry_sdk.start_span(
        op="gen_ai.handoff",
        name=f"handoff from {my_agent.name} to {other_agent.name}",
    ):
        # the handoff span just marks the handoff
        pass

    with sentry_sdk.start_span(op="gen_ai.invoke_agent", ...):
        #  .. some code ..
        other_agent.run()
        #  .. some code ..