> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Batch logging for your agent

> Manually log agent traces for frameworks that have already completed the LLM call and need to record it.

<Note>
  Weave for Agents is in public preview. Features, APIs, and the Agents view UI may change before general availability.
</Note>

For frameworks that have already completed the LLM call and only need to record it, use `weave.log_turn` and `weave.log_session`. All spans are created and ended immediately without keeping any context managers open.

Data logged this way can be historical. No live session is needed. Set `session_id` to any stable string that uniquely identifies the conversation. Turns that share the same `session_id` are grouped as a single session in the **Agents** view.

If you're building your own agent loop, use the real-time instrumentation APIs described in [Trace your agents](/weave/guides/tracking/trace-agents) instead.

## Log a turn

To record a single completed turn after it happens, use `weave.log_turn`. It accepts a fully-formed turn, including all LLM and tool spans.

<Tabs>
  <Tab title="Python">
    ```python lines highlight="1,3,28" theme={null}
    weave.init("[YOUR-TEAM]/[YOUR-PROJECT]")

    from weave.session.session import LLM, Message, Tool, Usage

    llm_span = LLM(
        model="gpt-4o",
        provider_name="openai",
        input_messages=[Message(role="user", content="What is the weather?")],
        output_messages=[Message(role="assistant", content="Let me check.")],
        usage=Usage(input_tokens=100, output_tokens=20),
    )

    tool_span = Tool(
        name="get_weather",
        arguments='{"city": "Tokyo"}',
        result='"24°C, sunny"',
    )

    llm_span2 = LLM(
        model="gpt-4o",
        provider_name="openai",
        input_messages=[Message(role="user", content="What is the weather?")],
        output_messages=[Message(role="assistant", content="It is 24°C and sunny.")],
        usage=Usage(input_tokens=150, output_tokens=30),
    )

    # Log a turn with all its spans.
    weave.log_turn(
        session_id="my-session-abc",
        agent_name="weather-bot",
        messages=[
            Message(role="user", content="What is the weather in Tokyo?"),
            Message(role="assistant", content="It is 24°C and sunny in Tokyo."),
        ],
        spans=[llm_span, tool_span, llm_span2],
    )
    ```
  </Tab>

  <Tab title="TypeScript">
    ```plaintext theme={null}
    This feature is not available in the TypeScript SDK yet.
    ```
  </Tab>
</Tabs>

`log_turn` returns a `LogResult` containing the trace IDs of the emitted spans.

An optional `model` parameter on `log_turn` sets the model on the turn's own span, not on the child LLM spans. Each `LLM` span carries its own `model` independently. If a turn uses multiple models, set `model` on `log_turn` to whichever you consider the primary model for that turn.

## Log a session

To bulk-import a complete, multi-turn session at once, use `weave.log_session`. The `turns` parameter accepts a list of `Turn` objects, each constructed the same way as the previous `log_turn` example.

<Tabs>
  <Tab title="Python">
    ```python lines theme={null}
    weave.log_session(
        session_id="my-session-abc",
        agent_name="weather-bot",
        turns=[turn_1, turn_2],
    )
    ```
  </Tab>

  <Tab title="TypeScript">
    ```plaintext theme={null}
    This feature is not available in the TypeScript SDK yet.
    ```
  </Tab>
</Tabs>