Usage

The Agents SDK automatically tracks token usage for every run. You can access it from the run context and use it to monitor costs, enforce limits, or record analytics.

What is tracked

• requests: number of LLM API calls made
• input_tokens: total input tokens sent
• output_tokens: total output tokens received
• total_tokens: input + output
• request_usage_entries: list of per-request usage breakdowns
• details:
  • input_tokens_details.cached_tokens
  • output_tokens_details.reasoning_tokens

Accessing usage from a run

After Runner.run(...), access usage via result.context_wrapper.usage.

result = await Runner.run(agent, "What's the weather in Tokyo?")
usage = result.context_wrapper.usage

print("Requests:", usage.requests)
print("Input tokens:", usage.input_tokens)
print("Output tokens:", usage.output_tokens)
print("Total tokens:", usage.total_tokens)

Usage is aggregated across all model calls during the run (including tool calls and handoffs).

Enabling usage with third-party adapters

Usage reporting varies across third-party adapters and provider backends. If you rely on adapter-backed models and need accurate result.context_wrapper.usage values:

• With AnyLLMModel, usage is propagated automatically when the upstream provider returns it. For streamed Chat Completions backends, you may need ModelSettings(include_usage=True) before usage chunks are emitted.
• With LitellmModel, some provider backends do not report usage by default, so ModelSettings(include_usage=True) is often required.

Review the adapter-specific notes in the Third-party adapters section of the Models guide and validate the exact provider backend you plan to deploy.

Per-request usage tracking

The SDK automatically tracks usage for each API request in request_usage_entries, useful for detailed cost calculation and monitoring context window consumption.

result = await Runner.run(agent, "What's the weather in Tokyo?")

for i, request in enumerate(result.context_wrapper.usage.request_usage_entries):
    print(f"Request {i + 1}: {request.input_tokens} in, {request.output_tokens} out")

Accessing usage with sessions

When you use a Session (e.g., SQLiteSession), each call to Runner.run(...) returns usage for that specific run. Sessions maintain conversation history for context, but each run's usage is independent.

session = SQLiteSession("my_conversation")

first = await Runner.run(agent, "Hi!", session=session)
print(first.context_wrapper.usage.total_tokens)  # Usage for first run

second = await Runner.run(agent, "Can you elaborate?", session=session)
print(second.context_wrapper.usage.total_tokens)  # Usage for second run

Note that while sessions preserve conversation context between runs, the usage metrics returned by each Runner.run() call represent only that particular execution. In sessions, previous messages may be re-fed as input to each run, which affects the input token count in consequent turns.

Using usage in hooks

If you're using RunHooks, the context object passed to each hook contains usage. This lets you log usage at key lifecycle moments.

class MyHooks(RunHooks):
    async def on_agent_end(self, context: RunContextWrapper, agent: Agent, output: Any) -> None:
        u = context.usage
        print(f"{agent.name} → {u.requests} requests, {u.total_tokens} total tokens")

API reference

For detailed API documentation, see:

• [Usage][agents.usage.Usage] - Usage tracking data structure
• [RequestUsage][agents.usage.RequestUsage] - Per-request usage details
• [RunContextWrapper][agents.run.RunContextWrapper] - Access usage from run context
• [RunHooks][agents.run.RunHooks] - Hook into usage tracking lifecycle