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Strands Agents → OpenObserve

Capture agent run timing, token usage, LLM call details, and event loop cycles for every Strands Agents invocation. Strands Agents emits OpenTelemetry spans automatically for each agent invocation, LLM call, and event loop cycle. Wrap agent calls in a manual root span to attach your own input and output attributes.

Prerequisites

  • Python 3.10+
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • An Anthropic API key

Installation

pip install openobserve-telemetry-sdk strands-agents python-dotenv

Configuration

Create a .env file in your project root:

OPENOBSERVE_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
ANTHROPIC_API_KEY=your-anthropic-api-key

Instrumentation

Call openobserve_init() to set up the tracer provider, then wrap each agent call in a manual span. Strands Agents automatically creates child spans for the agent invocation, event loop cycle, and LLM call.

from dotenv import load_dotenv
load_dotenv()

from openobserve import openobserve_init
openobserve_init()

from opentelemetry import trace
from strands import Agent
from strands.models.anthropic import AnthropicModel

tracer = trace.get_tracer(__name__)

model = AnthropicModel(model_id="claude-haiku-4-5-20251001", max_tokens=1000)
agent = Agent(model=model)

with tracer.start_as_current_span("strands.agent") as span:
    span.set_attribute("input_value", "What is OpenTelemetry?")
    response = agent("What is OpenTelemetry?")
    output = str(response)
    span.set_attribute("output_value", output[:200])

print(output)

What Gets Captured

Each agent call produces a tree of four spans. The manual strands.agent span is the root; Strands Agents creates the remaining three automatically.

strands.agent span (manual root)

Attribute Description
input_value Prompt passed to the agent
output_value First 200 characters of the agent response
duration End-to-end agent run latency
span_status UNSET on success, error status on failure

invoke_agent span (Strands built-in)

Attribute Description
gen_ai_agent_name Agent name (e.g. Strands Agents)
gen_ai_system strands-agents
gen_ai_request_model Model ID sent in the request
gen_ai_response_model Model ID that served the response
gen_ai_usage_input_tokens Prompt tokens across all LLM calls in the run
gen_ai_usage_output_tokens Completion tokens across all LLM calls
gen_ai_usage_total_tokens Total tokens for the full agent run
gen_ai_usage_cache_read_input_tokens Tokens served from prompt cache
gen_ai_usage_cache_write_input_tokens Tokens written to prompt cache
llm_observation_type AGENT
llm_output_finish_reason Reason the agent stopped (e.g. end_turn)
llm_output_message Final response text
llm_usage_tokens_input Input tokens (mirrored from gen_ai_usage_input_tokens)
llm_usage_tokens_output Output tokens (mirrored)
llm_usage_tokens_total Total tokens (mirrored)

execute_event_loop_cycle span (Strands built-in)

Attribute Description
gen_ai_system strands-agents
event_loop_cycle_id Unique ID for this reasoning cycle
llm_observation_type SPAN

chat span (Strands built-in, one per LLM call)

Attribute Description
gen_ai_system strands-agents
gen_ai_request_model Model ID sent in the request
gen_ai_response_model Model ID that served the response
gen_ai_server_time_to_first_token Time to first token in milliseconds
gen_ai_usage_input_tokens Prompt tokens for this call
gen_ai_usage_output_tokens Completion tokens for this call
gen_ai_usage_total_tokens Total tokens for this call
llm_observation_type GENERATION
llm_output_finish_reason Stop reason (e.g. end_turn)
llm_output_message Raw model output
llm_usage_tokens_input Input tokens
llm_usage_tokens_output Output tokens
llm_usage_tokens_total Total tokens
llm_usage_cost_input Estimated input cost
llm_usage_cost_output Estimated output cost

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces
  2. Filter by operation name strands.agent to find agent root spans
  3. Expand any trace to see the full span tree: strands.agent > invoke_agent > execute_event_loop_cycle > chat
  4. Click the chat span to inspect per-call token counts and time to first token
  5. Click the invoke_agent span to see cumulative token usage across all LLM calls in the run

Strands Agents trace in OpenObserve

Next Steps

With Strands Agents instrumented, every agent run is recorded in OpenObserve. From here you can track latency per agent, compare token usage across prompts, monitor cache hit rates via gen_ai_usage_cache_read_input_tokens, and set alerts on error spans.

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