agentmetrics-openai-agents 0.2.0

AgentMetrics observability integration for OpenAI Agents SDK

agentmetrics-openai-agents

AgentMetrics integration for the OpenAI Agents SDK. Register one trace processor at startup and every agent run reports back to your dashboard showing latency, cost, token counts, tool calls, and errors, with zero changes to your agent code.

---

Install

pip install agentmetrics-openai-agents

---

Quickstart

from agents.tracing import add_trace_processor
from agentmetrics_openai_agents import AgentMetricsProcessor

# register once at startup, covers all agents in the process
add_trace_processor(AgentMetricsProcessor(
    agent_id="my-openai-agent",
    base_url="http://localhost:8099",
))

# Run your agents as normal
result = await Runner.run(my_agent, "Summarize this document")

---

API

AgentMetricsProcessor(agent_id, base_url)

Parameter	Default	Description
agent_id	"openai-agent"	Fallback label if the trace has no name attribute
base_url	"http://localhost:8099"	AgentMetrics server address

Implements TracingProcessor from agents.tracing. Pass to add_trace_processor() before running any agents.

.force_flush()

Blocks until all in-flight HTTP requests complete.

.shutdown()

Calls force_flush(). Called automatically when the process exits cleanly.

---

What gets tracked

Each agent trace emits one event to /v1/events when on_trace_end fires:

Field	Description
status	success or failed
duration_ms	Wall-clock trace duration
input_tokens / output_tokens	Aggregated from all LLMSpanData spans
cache_read_tokens / cache_write_tokens	Cache token counts
llm_calls	Number of LLM spans in the trace
tool_calls / tool_errors	Counts from FunctionSpanData spans
tool_names	Set of function/tool names
model	Model name from the first LLM span output
estimated_cost_usd	Computed from token counts and model pricing
error	First 500 chars of the trace error on failure

---

License

MIT