wehub-resource-sync
87 lines
3.8 KiB
Markdown
87 lines
3.8 KiB
Markdown
# Usage
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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.
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## What is tracked
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- **requests**: number of LLM API calls made
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- **input_tokens**: total input tokens sent
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- **output_tokens**: total output tokens received
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- **total_tokens**: input + output
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- **request_usage_entries**: list of per-request usage breakdowns
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- **details**:
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- `input_tokens_details.cached_tokens`
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- `output_tokens_details.reasoning_tokens`
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## Accessing usage from a run
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After `Runner.run(...)`, access usage via `result.context_wrapper.usage`.
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```python
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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usage = result.context_wrapper.usage
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print("Requests:", usage.requests)
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print("Input tokens:", usage.input_tokens)
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print("Output tokens:", usage.output_tokens)
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print("Total tokens:", usage.total_tokens)
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```
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Usage is aggregated across all model calls during the run (including tool calls and handoffs).
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### Enabling usage with third-party adapters
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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:
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- 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.
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- With `LitellmModel`, some provider backends do not report usage by default, so `ModelSettings(include_usage=True)` is often required.
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Review the adapter-specific notes in the [Third-party adapters](models/index.md#third-party-adapters) section of the Models guide and validate the exact provider backend you plan to deploy.
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## Per-request usage tracking
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The SDK automatically tracks usage for each API request in `request_usage_entries`, useful for detailed cost calculation and monitoring context window consumption.
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```python
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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for i, request in enumerate(result.context_wrapper.usage.request_usage_entries):
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print(f"Request {i + 1}: {request.input_tokens} in, {request.output_tokens} out")
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```
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## Accessing usage with sessions
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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.
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```python
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session = SQLiteSession("my_conversation")
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first = await Runner.run(agent, "Hi!", session=session)
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print(first.context_wrapper.usage.total_tokens) # Usage for first run
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second = await Runner.run(agent, "Can you elaborate?", session=session)
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print(second.context_wrapper.usage.total_tokens) # Usage for second run
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```
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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 subsequent turns.
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## Using usage in hooks
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If you're using `RunHooks`, the `context` object passed to each hook contains `usage`. This lets you log usage at key lifecycle moments.
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```python
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class MyHooks(RunHooks):
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async def on_agent_end(self, context: RunContextWrapper, agent: Agent, output: Any) -> None:
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u = context.usage
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print(f"{agent.name} → {u.requests} requests, {u.total_tokens} total tokens")
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```
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## API reference
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For detailed API documentation, see:
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- [`Usage`][agents.usage.Usage] - Usage tracking data structure
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- [`RequestUsage`][agents.usage.RequestUsage] - Per-request usage details
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- [`RunContextWrapper`][agents.run.RunContextWrapper] - Access usage from run context
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- [`RunHooks`][agents.run.RunHooks] - Hook into usage tracking lifecycle
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