Instrument AI Agents

With Sentry AI Agent Monitoring, you can monitor and debug your AI systems with full-stack context. You'll be able to track key insights like token usage, latency, tool usage, and error rates. AI Agent Monitoring data will be fully connected to your other Sentry data like logs, errors, and traces.

npx @sentry/dotagents add getsentry/sentry-agent-skills --name sentry-setup-ai-monitoring

As a prerequisite to setting up AI Agent Monitoring with Python, you'll need to first set up tracing. Once this is done, the Python SDK will automatically instrument AI agents created with supported libraries. If that doesn't fit your use case, you can use custom instrumentation described below.

Automatic Instrumentation

The Python SDK supports automatic instrumentation for some AI libraries. We recommend adding their integrations to your Sentry configuration to automatically capture spans for AI agents.

• Anthropic
• Google Gen AI
• OpenAI
• OpenAI Agents SDK
• LangChain
• LangGraph
• LiteLLM
• Pydantic AI

Manual Instrumentation

For your AI agents data to show up in the Sentry AI Agents Insights, at least one of the AI spans needs to be created and have well-defined names and data attributes. See below.

The @sentry_sdk.trace() decorator can also be used to create these spans.

Spans

AI Request span

This span represents a request to a LLM model or service that generates a response based on the input prompt.

Example AI Request span

import sentry_sdk

messages = [{"role": "user", "content": "Tell me a joke"}]

with sentry_sdk.start_span(op="gen_ai.request", name="chat o3-mini") as span:
    span.set_data("gen_ai.request.model", "o3-mini")
    span.set_data("gen_ai.request.messages", json.dumps(messages))
    span.set_data("gen_ai.operation.name", "invoke_agent")

    # Call your LLM here
    result = client.chat.completions.create(model="o3-mini", messages=messages)

    span.set_data("gen_ai.response.text", json.dumps([result.choices[0].message.content]))
    # Set token usage
    span.set_data("gen_ai.usage.input_tokens", result.usage.prompt_tokens)
    span.set_data("gen_ai.usage.output_tokens", result.usage.completion_tokens)

Invoke Agent Span

This span represents the execution of an AI agent, capturing the full lifecycle from receiving a task to producing a final response.

Example of an Invoke Agent Span:

import sentry_sdk

with sentry_sdk.start_span(op="gen_ai.invoke_agent", name="invoke_agent Weather Agent") as span:
    span.set_data("gen_ai.request.model", "o3-mini")
    span.set_data("gen_ai.agent.name", "Weather Agent")

    # Run the agent
    final_output = my_agent.run()

    span.set_data("gen_ai.response.text", str(final_output))
    # Set token usage
    span.set_data("gen_ai.usage.input_tokens", result.usage.input_tokens)
    span.set_data("gen_ai.usage.output_tokens", result.usage.output_tokens)

Execute Tool Span

This span represents the execution of a tool or function that was requested by an AI model, including the input arguments and resulting output.

Example Execute Tool Span

import sentry_sdk

with sentry_sdk.start_span(op="gen_ai.execute_tool", name="execute_tool get_weather") as span:
    span.set_data("gen_ai.tool.name", "get_weather")
    span.set_data("gen_ai.tool.input", json.dumps({"location": "Paris"}))

    # Call the tool
    result = get_weather(location="Paris")

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

Handoff Span

This span marks the transition of control from one agent to another, typically when the current agent determines another agent is better suited to handle the task.

Example of a Handoff Span

import sentry_sdk

with sentry_sdk.start_span(op="gen_ai.handoff", name="handoff from Weather Agent to Travel Agent"):
    pass  # Handoff span just marks the transition

with sentry_sdk.start_span(op="gen_ai.invoke_agent", name="invoke_agent Travel Agent"):
    # Run the target agent here
    pass

Tracking Conversations

For AI applications that involve multi-turn conversations, you can use sentry_sdk.ai.set_conversation_id() to associate all AI spans from the same conversation. This enables you to track and analyze complete conversation flows within Sentry.

The conversation ID is set as the gen_ai.conversation.id attribute on all AI-related spans in the current scope. To remove the conversation ID, use the remove_conversation_id() method on the Scope.

import sentry_sdk.ai

sentry_sdk.ai.set_conversation_id("conv_abc123")

# All subsequent AI calls will be linked to this conversation

Some integrations, like the OpenAI integration, will automatically set the conversation ID for you, when you use APIs that expose that.

import sentry_sdk
import openai

sentry_sdk.init(...)

conversation = openai.conversations.create()

response = openai.responses.create(
    model="gpt-4.1",
    input=[{"role": "user", "content": "What are the 5 Ds of dodgeball?"}],
    conversation=conversation.id  # this will automatically set `gen_ai.conversation.id` on the span
)

Common Span Attributes

Some attributes are common to all AI Agents spans:

Token Usage and Cost Gotchas

When manually setting token attributes, be aware of how Sentry uses them to calculate model costs.

Cached and reasoning tokens are subsets, not separate counts. gen_ai.usage.input_tokens is the total input token count that already includes any cached tokens. Similarly, gen_ai.usage.output_tokens already includes reasoning tokens. Sentry subtracts the cached/reasoning counts from the totals to compute the "raw" portion, so reporting them incorrectly can produce wrong or negative costs.

For example, say your LLM call uses 100 input tokens total, 90 of which were served from cache. Using a standard rate of $0.01 per token and a cached rate of $0.001 per token:

Correct — input_tokens is the total (includes cached):

• gen_ai.usage.input_tokens = 100
• gen_ai.usage.input_tokens.cached = 90
• Sentry calculates: (100 - 90) × $0.01 + 90 × $0.001 = $0.10 + $0.09 = $0.19 ✓

Wrong — input_tokens set to only the non-cached tokens, making cached larger than total:

• gen_ai.usage.input_tokens = 10
• gen_ai.usage.input_tokens.cached = 90
• Sentry calculates: (10 - 90) × $0.01 + 90 × $0.001 = −$0.80 + $0.09 = −$0.71

Because input_tokens.cached (90) is larger than input_tokens (10), the subtraction goes negative, resulting in a negative total cost.

The same applies to gen_ai.usage.output_tokens and gen_ai.usage.output_tokens.reasoning.