While decorators offer a concise way to instrument functions, you might prefer or need to manually manage trace and span lifecycles. This is useful in asynchronous contexts, for finer control, or when decorators are inconvenient. The LangWatch Python SDK provides two primary ways to do this manually:

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Using Context Managers (with/async with)

The langwatch.trace() and langwatch.span() functions can be used directly as asynchronous (async with) or synchronous (with) context managers. This is the recommended approach for manual instrumentation as it automatically handles ending the trace/span, even if errors occur. Here’s how you can achieve the same instrumentation as the decorator examples, but using context managers: 
import langwatch
from langwatch.types import RAGChunk
from langwatch.attributes import AttributeKey # For semantic attribute keys
import asyncio # Assuming async operation

langwatch.setup()

async def rag_retrieval_manual(query: str):
    # Use async with for the span, instead of a decorator
    async with langwatch.span(type="rag", name="RAG Document Retrieval") as span:
        # ... your async retrieval logic ...
        await asyncio.sleep(0.05) # Simulate async work
        search_results = [
            {"id": "doc-1", "content": "Content for doc 1."},
            {"id": "doc-2", "content": "Content for doc 2."},
        ]

        # Update the span with input, context, metadata, and output
        span.update(
            input=query,
            contexts=[
                RAGChunk(document_id=doc["id"], content=doc["content"])
                for doc in search_results
            ],
            output=search_results,
            strategy="manual_vector_search"
        )
        return search_results

async def handle_user_query_manual(query: str):
    # Use async with for the trace
    async with langwatch.trace(name="Manual User Query Handling", metadata={"user_id": "manual-user", "query": query}) as trace:
        # Call the manually instrumented RAG function
        retrieved_docs = await rag_retrieval_manual(query)

        # --- Simulate LLM Call Step (manual span) ---
        llm_response = ""
        async with langwatch.span(type="llm", name="Manual LLM Generation") as llm_span:
            llm_input = {"role": "user", "content": f"Context: {retrieved_docs}\nQuery: {query}"}
            llm_metadata = {"model_name": "gpt-4o-mini"}

            # ... your async LLM call logic ...
            await asyncio.sleep(0.1)
            llm_response = "This is the manual LLM response."
            llm_output = {"role": "assistant", "content": llm_response}

            # Set input, metadata and output via update
            llm_span.update(
                input=llm_input,
                output=llm_output
                llm_metadata=llm_metadata,
            )

        # Set final trace output via update
        trace.update(output=llm_response)
        return llm_response

# Example execution (in an async context)
async def main():
    result = await handle_user_query_manual("Tell me about manual tracing with context managers.")
    print(result)
asyncio.run(main())
Key points for manual instrumentation with context managers:
  • Use with langwatch.trace(...) or async with langwatch.trace(...) to start a trace.
  • Use with langwatch.span(...) or async with langwatch.span(...) inside a trace block to create nested spans.
  • The trace or span object is available in the as trace: or as span: part of the with statement.
  • Use methods like span.add_event(), and primarily span.update(...) / trace.update(...) to add details. The update() method is flexible for adding structured data like input, output, metadata, and contexts.
  • This approach gives explicit control over the start and end of each instrumented block, as the context manager handles ending the span automatically.

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Direct Span Creation (span.end())

Alternatively, you can manage span and trace lifecycles completely manually. Call langwatch.span() or langwatch.trace() directly to start them, and then explicitly call the end() method on the returned object (span.end() or trace.end()) when the operation finishes. This requires careful handling to ensure end() is always called, even if errors occur (e.g., using try...finally). Context managers are generally preferred as they handle this automatically. 
import langwatch
import time

# Assume langwatch.setup() and a trace context exist

def process_data_manually(data):
    span = langwatch.span(name="Manual Data Processing") # Start the span
    try:
        span.update(input=data)
        # ... synchronous processing logic ...
        time.sleep(0.02)
        result = f"Processed: {data}"
        span.update(output=result)
        return result
    except Exception as e:
        span.record_exception(e) # Record exceptions
        span.set_status("error", description=str(e))
        raise # Re-raise the exception
    finally:
        span.end() # CRITICAL: Ensure the span is ended

# with langwatch.trace(): # Needs to be within a trace
#     processed = process_data_manually("some data")