116class Langfuse:
 117    """Main client for Langfuse tracing and platform features.
 118
 119    This class provides an interface for creating and managing traces, spans,
 120    and generations in Langfuse as well as interacting with the Langfuse API.
 121
 122    The client features a thread-safe singleton pattern for each unique public API key,
 123    ensuring consistent trace context propagation across your application. It implements
 124    efficient batching of spans with configurable flush settings and includes background
 125    thread management for media uploads and score ingestion.
 126
 127    Configuration is flexible through either direct parameters or environment variables,
 128    with graceful fallbacks and runtime configuration updates.
 129
 130    Attributes:
 131        api: Synchronous API client for Langfuse backend communication
 132        async_api: Asynchronous API client for Langfuse backend communication
 133        _otel_tracer: Internal LangfuseTracer instance managing OpenTelemetry components
 134
 135    Parameters:
 136        public_key (Optional[str]): Your Langfuse public API key. Can also be set via LANGFUSE_PUBLIC_KEY environment variable.
 137        secret_key (Optional[str]): Your Langfuse secret API key. Can also be set via LANGFUSE_SECRET_KEY environment variable.
 138        host (Optional[str]): The Langfuse API host URL. Defaults to "https://cloud.langfuse.com". Can also be set via LANGFUSE_HOST environment variable.
 139        timeout (Optional[int]): Timeout in seconds for API requests. Defaults to 5 seconds.
 140        httpx_client (Optional[httpx.Client]): Custom httpx client for making non-tracing HTTP requests. If not provided, a default client will be created.
 141        debug (bool): Enable debug logging. Defaults to False. Can also be set via LANGFUSE_DEBUG environment variable.
 142        tracing_enabled (Optional[bool]): Enable or disable tracing. Defaults to True. Can also be set via LANGFUSE_TRACING_ENABLED environment variable.
 143        flush_at (Optional[int]): Number of spans to batch before sending to the API. Defaults to 512. Can also be set via LANGFUSE_FLUSH_AT environment variable.
 144        flush_interval (Optional[float]): Time in seconds between batch flushes. Defaults to 5 seconds. Can also be set via LANGFUSE_FLUSH_INTERVAL environment variable.
 145        environment (Optional[str]): Environment name for tracing. Default is 'default'. Can also be set via LANGFUSE_TRACING_ENVIRONMENT environment variable. Can be any lowercase alphanumeric string with hyphens and underscores that does not start with 'langfuse'.
 146        release (Optional[str]): Release version/hash of your application. Used for grouping analytics by release.
 147        media_upload_thread_count (Optional[int]): Number of background threads for handling media uploads. Defaults to 1. Can also be set via LANGFUSE_MEDIA_UPLOAD_THREAD_COUNT environment variable.
 148        sample_rate (Optional[float]): Sampling rate for traces (0.0 to 1.0). Defaults to 1.0 (100% of traces are sampled). Can also be set via LANGFUSE_SAMPLE_RATE environment variable.
 149        mask (Optional[MaskFunction]): Function to mask sensitive data in traces before sending to the API.
 150        blocked_instrumentation_scopes (Optional[List[str]]): List of instrumentation scope names to block from being exported to Langfuse. Spans from these scopes will be filtered out before being sent to the API. Useful for filtering out spans from specific libraries or frameworks. For exported spans, you can see the instrumentation scope name in the span metadata in Langfuse (`metadata.scope.name`)
 151        additional_headers (Optional[Dict[str, str]]): Additional headers to include in all API requests and OTLPSpanExporter requests. These headers will be merged with default headers. Note: If httpx_client is provided, additional_headers must be set directly on your custom httpx_client as well.
 152        tracer_provider(Optional[TracerProvider]): OpenTelemetry TracerProvider to use for Langfuse. This can be useful to set to have disconnected tracing between Langfuse and other OpenTelemetry-span emitting libraries. Note: To track active spans, the context is still shared between TracerProviders. This may lead to broken trace trees.
 153
 154    Example:
 155        ```python
 156        from langfuse.otel import Langfuse
 157
 158        # Initialize the client (reads from env vars if not provided)
 159        langfuse = Langfuse(
 160            public_key="your-public-key",
 161            secret_key="your-secret-key",
 162            host="https://cloud.langfuse.com",  # Optional, default shown
 163        )
 164
 165        # Create a trace span
 166        with langfuse.start_as_current_span(name="process-query") as span:
 167            # Your application code here
 168
 169            # Create a nested generation span for an LLM call
 170            with span.start_as_current_generation(
 171                name="generate-response",
 172                model="gpt-4",
 173                input={"query": "Tell me about AI"},
 174                model_parameters={"temperature": 0.7, "max_tokens": 500}
 175            ) as generation:
 176                # Generate response here
 177                response = "AI is a field of computer science..."
 178
 179                generation.update(
 180                    output=response,
 181                    usage_details={"prompt_tokens": 10, "completion_tokens": 50},
 182                    cost_details={"total_cost": 0.0023}
 183                )
 184
 185                # Score the generation (supports NUMERIC, BOOLEAN, CATEGORICAL)
 186                generation.score(name="relevance", value=0.95, data_type="NUMERIC")
 187        ```
 188    """
 189
 190    _resources: Optional[LangfuseResourceManager] = None
 191    _mask: Optional[MaskFunction] = None
 192    _otel_tracer: otel_trace_api.Tracer
 193
 194    def __init__(
 195        self,
 196        *,
 197        public_key: Optional[str] = None,
 198        secret_key: Optional[str] = None,
 199        host: Optional[str] = None,
 200        timeout: Optional[int] = None,
 201        httpx_client: Optional[httpx.Client] = None,
 202        debug: bool = False,
 203        tracing_enabled: Optional[bool] = True,
 204        flush_at: Optional[int] = None,
 205        flush_interval: Optional[float] = None,
 206        environment: Optional[str] = None,
 207        release: Optional[str] = None,
 208        media_upload_thread_count: Optional[int] = None,
 209        sample_rate: Optional[float] = None,
 210        mask: Optional[MaskFunction] = None,
 211        blocked_instrumentation_scopes: Optional[List[str]] = None,
 212        additional_headers: Optional[Dict[str, str]] = None,
 213        tracer_provider: Optional[TracerProvider] = None,
 214    ):
 215        self._host = host or cast(
 216            str, os.environ.get(LANGFUSE_HOST, "https://cloud.langfuse.com")
 217        )
 218        self._environment = environment or cast(
 219            str, os.environ.get(LANGFUSE_TRACING_ENVIRONMENT)
 220        )
 221        self._project_id: Optional[str] = None
 222        sample_rate = sample_rate or float(os.environ.get(LANGFUSE_SAMPLE_RATE, 1.0))
 223        if not 0.0 <= sample_rate <= 1.0:
 224            raise ValueError(
 225                f"Sample rate must be between 0.0 and 1.0, got {sample_rate}"
 226            )
 227
 228        timeout = timeout or int(os.environ.get(LANGFUSE_TIMEOUT, 5))
 229
 230        self._tracing_enabled = (
 231            tracing_enabled
 232            and os.environ.get(LANGFUSE_TRACING_ENABLED, "true").lower() != "false"
 233        )
 234        if not self._tracing_enabled:
 235            langfuse_logger.info(
 236                "Configuration: Langfuse tracing is explicitly disabled. No data will be sent to the Langfuse API."
 237            )
 238
 239        debug = (
 240            debug if debug else (os.getenv(LANGFUSE_DEBUG, "false").lower() == "true")
 241        )
 242        if debug:
 243            logging.basicConfig(
 244                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 245            )
 246            langfuse_logger.setLevel(logging.DEBUG)
 247
 248        public_key = public_key or os.environ.get(LANGFUSE_PUBLIC_KEY)
 249        if public_key is None:
 250            langfuse_logger.warning(
 251                "Authentication error: Langfuse client initialized without public_key. Client will be disabled. "
 252                "Provide a public_key parameter or set LANGFUSE_PUBLIC_KEY environment variable. "
 253            )
 254            self._otel_tracer = otel_trace_api.NoOpTracer()
 255            return
 256
 257        secret_key = secret_key or os.environ.get(LANGFUSE_SECRET_KEY)
 258        if secret_key is None:
 259            langfuse_logger.warning(
 260                "Authentication error: Langfuse client initialized without secret_key. Client will be disabled. "
 261                "Provide a secret_key parameter or set LANGFUSE_SECRET_KEY environment variable. "
 262            )
 263            self._otel_tracer = otel_trace_api.NoOpTracer()
 264            return
 265
 266        if os.environ.get("OTEL_SDK_DISABLED", "false").lower() == "true":
 267            langfuse_logger.warning(
 268                "OTEL_SDK_DISABLED is set. Langfuse tracing will be disabled and no traces will appear in the UI."
 269            )
 270
 271        # Initialize api and tracer if requirements are met
 272        self._resources = LangfuseResourceManager(
 273            public_key=public_key,
 274            secret_key=secret_key,
 275            host=self._host,
 276            timeout=timeout,
 277            environment=self._environment,
 278            release=release,
 279            flush_at=flush_at,
 280            flush_interval=flush_interval,
 281            httpx_client=httpx_client,
 282            media_upload_thread_count=media_upload_thread_count,
 283            sample_rate=sample_rate,
 284            mask=mask,
 285            tracing_enabled=self._tracing_enabled,
 286            blocked_instrumentation_scopes=blocked_instrumentation_scopes,
 287            additional_headers=additional_headers,
 288            tracer_provider=tracer_provider,
 289        )
 290        self._mask = self._resources.mask
 291
 292        self._otel_tracer = (
 293            self._resources.tracer
 294            if self._tracing_enabled and self._resources.tracer is not None
 295            else otel_trace_api.NoOpTracer()
 296        )
 297        self.api = self._resources.api
 298        self.async_api = self._resources.async_api
 299
 300    def start_span(
 301        self,
 302        *,
 303        trace_context: Optional[TraceContext] = None,
 304        name: str,
 305        input: Optional[Any] = None,
 306        output: Optional[Any] = None,
 307        metadata: Optional[Any] = None,
 308        version: Optional[str] = None,
 309        level: Optional[SpanLevel] = None,
 310        status_message: Optional[str] = None,
 311    ) -> LangfuseSpan:
 312        """Create a new span for tracing a unit of work.
 313
 314        This method creates a new span but does not set it as the current span in the
 315        context. To create and use a span within a context, use start_as_current_span().
 316
 317        The created span will be the child of the current span in the context.
 318
 319        Args:
 320            trace_context: Optional context for connecting to an existing trace
 321            name: Name of the span (e.g., function or operation name)
 322            input: Input data for the operation (can be any JSON-serializable object)
 323            output: Output data from the operation (can be any JSON-serializable object)
 324            metadata: Additional metadata to associate with the span
 325            version: Version identifier for the code or component
 326            level: Importance level of the span (info, warning, error)
 327            status_message: Optional status message for the span
 328
 329        Returns:
 330            A LangfuseSpan object that must be ended with .end() when the operation completes
 331
 332        Example:
 333            ```python
 334            span = langfuse.start_span(name="process-data")
 335            try:
 336                # Do work
 337                span.update(output="result")
 338            finally:
 339                span.end()
 340            ```
 341        """
 342        return self.start_observation(
 343            trace_context=trace_context,
 344            name=name,
 345            as_type="span",
 346            input=input,
 347            output=output,
 348            metadata=metadata,
 349            version=version,
 350            level=level,
 351            status_message=status_message,
 352        )
 353
 354    def start_as_current_span(
 355        self,
 356        *,
 357        trace_context: Optional[TraceContext] = None,
 358        name: str,
 359        input: Optional[Any] = None,
 360        output: Optional[Any] = None,
 361        metadata: Optional[Any] = None,
 362        version: Optional[str] = None,
 363        level: Optional[SpanLevel] = None,
 364        status_message: Optional[str] = None,
 365        end_on_exit: Optional[bool] = None,
 366    ) -> _AgnosticContextManager[LangfuseSpan]:
 367        """Create a new span and set it as the current span in a context manager.
 368
 369        This method creates a new span and sets it as the current span within a context
 370        manager. Use this method with a 'with' statement to automatically handle span
 371        lifecycle within a code block.
 372
 373        The created span will be the child of the current span in the context.
 374
 375        Args:
 376            trace_context: Optional context for connecting to an existing trace
 377            name: Name of the span (e.g., function or operation name)
 378            input: Input data for the operation (can be any JSON-serializable object)
 379            output: Output data from the operation (can be any JSON-serializable object)
 380            metadata: Additional metadata to associate with the span
 381            version: Version identifier for the code or component
 382            level: Importance level of the span (info, warning, error)
 383            status_message: Optional status message for the span
 384            end_on_exit (default: True): Whether to end the span automatically when leaving the context manager. If False, the span must be manually ended to avoid memory leaks.
 385
 386        Returns:
 387            A context manager that yields a LangfuseSpan
 388
 389        Example:
 390            ```python
 391            with langfuse.start_as_current_span(name="process-query") as span:
 392                # Do work
 393                result = process_data()
 394                span.update(output=result)
 395
 396                # Create a child span automatically
 397                with span.start_as_current_span(name="sub-operation") as child_span:
 398                    # Do sub-operation work
 399                    child_span.update(output="sub-result")
 400            ```
 401        """
 402        return self.start_as_current_observation(
 403            trace_context=trace_context,
 404            name=name,
 405            as_type="span",
 406            input=input,
 407            output=output,
 408            metadata=metadata,
 409            version=version,
 410            level=level,
 411            status_message=status_message,
 412            end_on_exit=end_on_exit,
 413        )
 414
 415    @overload
 416    def start_observation(
 417        self,
 418        *,
 419        trace_context: Optional[TraceContext] = None,
 420        name: str,
 421        as_type: Literal["generation"],
 422        input: Optional[Any] = None,
 423        output: Optional[Any] = None,
 424        metadata: Optional[Any] = None,
 425        version: Optional[str] = None,
 426        level: Optional[SpanLevel] = None,
 427        status_message: Optional[str] = None,
 428        completion_start_time: Optional[datetime] = None,
 429        model: Optional[str] = None,
 430        model_parameters: Optional[Dict[str, MapValue]] = None,
 431        usage_details: Optional[Dict[str, int]] = None,
 432        cost_details: Optional[Dict[str, float]] = None,
 433        prompt: Optional[PromptClient] = None,
 434    ) -> LangfuseGeneration: ...
 435
 436    @overload
 437    def start_observation(
 438        self,
 439        *,
 440        trace_context: Optional[TraceContext] = None,
 441        name: str,
 442        as_type: Literal["span"] = "span",
 443        input: Optional[Any] = None,
 444        output: Optional[Any] = None,
 445        metadata: Optional[Any] = None,
 446        version: Optional[str] = None,
 447        level: Optional[SpanLevel] = None,
 448        status_message: Optional[str] = None,
 449    ) -> LangfuseSpan: ...
 450
 451    @overload
 452    def start_observation(
 453        self,
 454        *,
 455        trace_context: Optional[TraceContext] = None,
 456        name: str,
 457        as_type: Literal["agent"],
 458        input: Optional[Any] = None,
 459        output: Optional[Any] = None,
 460        metadata: Optional[Any] = None,
 461        version: Optional[str] = None,
 462        level: Optional[SpanLevel] = None,
 463        status_message: Optional[str] = None,
 464    ) -> LangfuseAgent: ...
 465
 466    @overload
 467    def start_observation(
 468        self,
 469        *,
 470        trace_context: Optional[TraceContext] = None,
 471        name: str,
 472        as_type: Literal["tool"],
 473        input: Optional[Any] = None,
 474        output: Optional[Any] = None,
 475        metadata: Optional[Any] = None,
 476        version: Optional[str] = None,
 477        level: Optional[SpanLevel] = None,
 478        status_message: Optional[str] = None,
 479    ) -> LangfuseTool: ...
 480
 481    @overload
 482    def start_observation(
 483        self,
 484        *,
 485        trace_context: Optional[TraceContext] = None,
 486        name: str,
 487        as_type: Literal["chain"],
 488        input: Optional[Any] = None,
 489        output: Optional[Any] = None,
 490        metadata: Optional[Any] = None,
 491        version: Optional[str] = None,
 492        level: Optional[SpanLevel] = None,
 493        status_message: Optional[str] = None,
 494    ) -> LangfuseChain: ...
 495
 496    @overload
 497    def start_observation(
 498        self,
 499        *,
 500        trace_context: Optional[TraceContext] = None,
 501        name: str,
 502        as_type: Literal["retriever"],
 503        input: Optional[Any] = None,
 504        output: Optional[Any] = None,
 505        metadata: Optional[Any] = None,
 506        version: Optional[str] = None,
 507        level: Optional[SpanLevel] = None,
 508        status_message: Optional[str] = None,
 509    ) -> LangfuseRetriever: ...
 510
 511    @overload
 512    def start_observation(
 513        self,
 514        *,
 515        trace_context: Optional[TraceContext] = None,
 516        name: str,
 517        as_type: Literal["evaluator"],
 518        input: Optional[Any] = None,
 519        output: Optional[Any] = None,
 520        metadata: Optional[Any] = None,
 521        version: Optional[str] = None,
 522        level: Optional[SpanLevel] = None,
 523        status_message: Optional[str] = None,
 524    ) -> LangfuseEvaluator: ...
 525
 526    @overload
 527    def start_observation(
 528        self,
 529        *,
 530        trace_context: Optional[TraceContext] = None,
 531        name: str,
 532        as_type: Literal["embedding"],
 533        input: Optional[Any] = None,
 534        output: Optional[Any] = None,
 535        metadata: Optional[Any] = None,
 536        version: Optional[str] = None,
 537        level: Optional[SpanLevel] = None,
 538        status_message: Optional[str] = None,
 539        completion_start_time: Optional[datetime] = None,
 540        model: Optional[str] = None,
 541        model_parameters: Optional[Dict[str, MapValue]] = None,
 542        usage_details: Optional[Dict[str, int]] = None,
 543        cost_details: Optional[Dict[str, float]] = None,
 544        prompt: Optional[PromptClient] = None,
 545    ) -> LangfuseEmbedding: ...
 546
 547    @overload
 548    def start_observation(
 549        self,
 550        *,
 551        trace_context: Optional[TraceContext] = None,
 552        name: str,
 553        as_type: Literal["guardrail"],
 554        input: Optional[Any] = None,
 555        output: Optional[Any] = None,
 556        metadata: Optional[Any] = None,
 557        version: Optional[str] = None,
 558        level: Optional[SpanLevel] = None,
 559        status_message: Optional[str] = None,
 560    ) -> LangfuseGuardrail: ...
 561
 562    def start_observation(
 563        self,
 564        *,
 565        trace_context: Optional[TraceContext] = None,
 566        name: str,
 567        as_type: ObservationTypeLiteralNoEvent = "span",
 568        input: Optional[Any] = None,
 569        output: Optional[Any] = None,
 570        metadata: Optional[Any] = None,
 571        version: Optional[str] = None,
 572        level: Optional[SpanLevel] = None,
 573        status_message: Optional[str] = None,
 574        completion_start_time: Optional[datetime] = None,
 575        model: Optional[str] = None,
 576        model_parameters: Optional[Dict[str, MapValue]] = None,
 577        usage_details: Optional[Dict[str, int]] = None,
 578        cost_details: Optional[Dict[str, float]] = None,
 579        prompt: Optional[PromptClient] = None,
 580    ) -> Union[
 581        LangfuseSpan,
 582        LangfuseGeneration,
 583        LangfuseAgent,
 584        LangfuseTool,
 585        LangfuseChain,
 586        LangfuseRetriever,
 587        LangfuseEvaluator,
 588        LangfuseEmbedding,
 589        LangfuseGuardrail,
 590    ]:
 591        """Create a new observation of the specified type.
 592
 593        This method creates a new observation but does not set it as the current span in the
 594        context. To create and use an observation within a context, use start_as_current_observation().
 595
 596        Args:
 597            trace_context: Optional context for connecting to an existing trace
 598            name: Name of the observation
 599            as_type: Type of observation to create (defaults to "span")
 600            input: Input data for the operation
 601            output: Output data from the operation
 602            metadata: Additional metadata to associate with the observation
 603            version: Version identifier for the code or component
 604            level: Importance level of the observation
 605            status_message: Optional status message for the observation
 606            completion_start_time: When the model started generating (for generation types)
 607            model: Name/identifier of the AI model used (for generation types)
 608            model_parameters: Parameters used for the model (for generation types)
 609            usage_details: Token usage information (for generation types)
 610            cost_details: Cost information (for generation types)
 611            prompt: Associated prompt template (for generation types)
 612
 613        Returns:
 614            An observation object of the appropriate type that must be ended with .end()
 615        """
 616        if trace_context:
 617            trace_id = trace_context.get("trace_id", None)
 618            parent_span_id = trace_context.get("parent_span_id", None)
 619
 620            if trace_id:
 621                remote_parent_span = self._create_remote_parent_span(
 622                    trace_id=trace_id, parent_span_id=parent_span_id
 623                )
 624
 625                with otel_trace_api.use_span(
 626                    cast(otel_trace_api.Span, remote_parent_span)
 627                ):
 628                    otel_span = self._otel_tracer.start_span(name=name)
 629                    otel_span.set_attribute(LangfuseOtelSpanAttributes.AS_ROOT, True)
 630
 631                    return self._create_observation_from_otel_span(
 632                        otel_span=otel_span,
 633                        as_type=as_type,
 634                        input=input,
 635                        output=output,
 636                        metadata=metadata,
 637                        version=version,
 638                        level=level,
 639                        status_message=status_message,
 640                        completion_start_time=completion_start_time,
 641                        model=model,
 642                        model_parameters=model_parameters,
 643                        usage_details=usage_details,
 644                        cost_details=cost_details,
 645                        prompt=prompt,
 646                    )
 647
 648        otel_span = self._otel_tracer.start_span(name=name)
 649
 650        return self._create_observation_from_otel_span(
 651            otel_span=otel_span,
 652            as_type=as_type,
 653            input=input,
 654            output=output,
 655            metadata=metadata,
 656            version=version,
 657            level=level,
 658            status_message=status_message,
 659            completion_start_time=completion_start_time,
 660            model=model,
 661            model_parameters=model_parameters,
 662            usage_details=usage_details,
 663            cost_details=cost_details,
 664            prompt=prompt,
 665        )
 666
 667    def _create_observation_from_otel_span(
 668        self,
 669        *,
 670        otel_span: otel_trace_api.Span,
 671        as_type: ObservationTypeLiteralNoEvent,
 672        input: Optional[Any] = None,
 673        output: Optional[Any] = None,
 674        metadata: Optional[Any] = None,
 675        version: Optional[str] = None,
 676        level: Optional[SpanLevel] = None,
 677        status_message: Optional[str] = None,
 678        completion_start_time: Optional[datetime] = None,
 679        model: Optional[str] = None,
 680        model_parameters: Optional[Dict[str, MapValue]] = None,
 681        usage_details: Optional[Dict[str, int]] = None,
 682        cost_details: Optional[Dict[str, float]] = None,
 683        prompt: Optional[PromptClient] = None,
 684    ) -> Union[
 685        LangfuseSpan,
 686        LangfuseGeneration,
 687        LangfuseAgent,
 688        LangfuseTool,
 689        LangfuseChain,
 690        LangfuseRetriever,
 691        LangfuseEvaluator,
 692        LangfuseEmbedding,
 693        LangfuseGuardrail,
 694    ]:
 695        """Create the appropriate observation type from an OTEL span."""
 696        if as_type in get_observation_types_list(ObservationTypeGenerationLike):
 697            observation_class = self._get_span_class(as_type)
 698            # Type ignore to prevent overloads of internal _get_span_class function,
 699            # issue is that LangfuseEvent could be returned and that classes have diff. args
 700            return observation_class(  # type: ignore[return-value,call-arg]
 701                otel_span=otel_span,
 702                langfuse_client=self,
 703                environment=self._environment,
 704                input=input,
 705                output=output,
 706                metadata=metadata,
 707                version=version,
 708                level=level,
 709                status_message=status_message,
 710                completion_start_time=completion_start_time,
 711                model=model,
 712                model_parameters=model_parameters,
 713                usage_details=usage_details,
 714                cost_details=cost_details,
 715                prompt=prompt,
 716            )
 717        else:
 718            # For other types (e.g. span, guardrail), create appropriate class without generation properties
 719            observation_class = self._get_span_class(as_type)
 720            # Type ignore to prevent overloads of internal _get_span_class function,
 721            # issue is that LangfuseEvent could be returned and that classes have diff. args
 722            return observation_class(  # type: ignore[return-value,call-arg]
 723                otel_span=otel_span,
 724                langfuse_client=self,
 725                environment=self._environment,
 726                input=input,
 727                output=output,
 728                metadata=metadata,
 729                version=version,
 730                level=level,
 731                status_message=status_message,
 732            )
 733            # span._observation_type = as_type
 734            # span._otel_span.set_attribute("langfuse.observation.type", as_type)
 735            # return span
 736
 737    def start_generation(
 738        self,
 739        *,
 740        trace_context: Optional[TraceContext] = None,
 741        name: str,
 742        input: Optional[Any] = None,
 743        output: Optional[Any] = None,
 744        metadata: Optional[Any] = None,
 745        version: Optional[str] = None,
 746        level: Optional[SpanLevel] = None,
 747        status_message: Optional[str] = None,
 748        completion_start_time: Optional[datetime] = None,
 749        model: Optional[str] = None,
 750        model_parameters: Optional[Dict[str, MapValue]] = None,
 751        usage_details: Optional[Dict[str, int]] = None,
 752        cost_details: Optional[Dict[str, float]] = None,
 753        prompt: Optional[PromptClient] = None,
 754    ) -> LangfuseGeneration:
 755        """Create a new generation span for model generations.
 756
 757        DEPRECATED: This method is deprecated and will be removed in a future version.
 758        Use start_observation(as_type='generation') instead.
 759
 760        This method creates a specialized span for tracking model generations.
 761        It includes additional fields specific to model generations such as model name,
 762        token usage, and cost details.
 763
 764        The created generation span will be the child of the current span in the context.
 765
 766        Args:
 767            trace_context: Optional context for connecting to an existing trace
 768            name: Name of the generation operation
 769            input: Input data for the model (e.g., prompts)
 770            output: Output from the model (e.g., completions)
 771            metadata: Additional metadata to associate with the generation
 772            version: Version identifier for the model or component
 773            level: Importance level of the generation (info, warning, error)
 774            status_message: Optional status message for the generation
 775            completion_start_time: When the model started generating the response
 776            model: Name/identifier of the AI model used (e.g., "gpt-4")
 777            model_parameters: Parameters used for the model (e.g., temperature, max_tokens)
 778            usage_details: Token usage information (e.g., prompt_tokens, completion_tokens)
 779            cost_details: Cost information for the model call
 780            prompt: Associated prompt template from Langfuse prompt management
 781
 782        Returns:
 783            A LangfuseGeneration object that must be ended with .end() when complete
 784
 785        Example:
 786            ```python
 787            generation = langfuse.start_generation(
 788                name="answer-generation",
 789                model="gpt-4",
 790                input={"prompt": "Explain quantum computing"},
 791                model_parameters={"temperature": 0.7}
 792            )
 793            try:
 794                # Call model API
 795                response = llm.generate(...)
 796
 797                generation.update(
 798                    output=response.text,
 799                    usage_details={
 800                        "prompt_tokens": response.usage.prompt_tokens,
 801                        "completion_tokens": response.usage.completion_tokens
 802                    }
 803                )
 804            finally:
 805                generation.end()
 806            ```
 807        """
 808        warnings.warn(
 809            "start_generation is deprecated and will be removed in a future version. "
 810            "Use start_observation(as_type='generation') instead.",
 811            DeprecationWarning,
 812            stacklevel=2,
 813        )
 814        return self.start_observation(
 815            trace_context=trace_context,
 816            name=name,
 817            as_type="generation",
 818            input=input,
 819            output=output,
 820            metadata=metadata,
 821            version=version,
 822            level=level,
 823            status_message=status_message,
 824            completion_start_time=completion_start_time,
 825            model=model,
 826            model_parameters=model_parameters,
 827            usage_details=usage_details,
 828            cost_details=cost_details,
 829            prompt=prompt,
 830        )
 831
 832    def start_as_current_generation(
 833        self,
 834        *,
 835        trace_context: Optional[TraceContext] = None,
 836        name: str,
 837        input: Optional[Any] = None,
 838        output: Optional[Any] = None,
 839        metadata: Optional[Any] = None,
 840        version: Optional[str] = None,
 841        level: Optional[SpanLevel] = None,
 842        status_message: Optional[str] = None,
 843        completion_start_time: Optional[datetime] = None,
 844        model: Optional[str] = None,
 845        model_parameters: Optional[Dict[str, MapValue]] = None,
 846        usage_details: Optional[Dict[str, int]] = None,
 847        cost_details: Optional[Dict[str, float]] = None,
 848        prompt: Optional[PromptClient] = None,
 849        end_on_exit: Optional[bool] = None,
 850    ) -> _AgnosticContextManager[LangfuseGeneration]:
 851        """Create a new generation span and set it as the current span in a context manager.
 852
 853        DEPRECATED: This method is deprecated and will be removed in a future version.
 854        Use start_as_current_observation(as_type='generation') instead.
 855
 856        This method creates a specialized span for model generations and sets it as the
 857        current span within a context manager. Use this method with a 'with' statement to
 858        automatically handle the generation span lifecycle within a code block.
 859
 860        The created generation span will be the child of the current span in the context.
 861
 862        Args:
 863            trace_context: Optional context for connecting to an existing trace
 864            name: Name of the generation operation
 865            input: Input data for the model (e.g., prompts)
 866            output: Output from the model (e.g., completions)
 867            metadata: Additional metadata to associate with the generation
 868            version: Version identifier for the model or component
 869            level: Importance level of the generation (info, warning, error)
 870            status_message: Optional status message for the generation
 871            completion_start_time: When the model started generating the response
 872            model: Name/identifier of the AI model used (e.g., "gpt-4")
 873            model_parameters: Parameters used for the model (e.g., temperature, max_tokens)
 874            usage_details: Token usage information (e.g., prompt_tokens, completion_tokens)
 875            cost_details: Cost information for the model call
 876            prompt: Associated prompt template from Langfuse prompt management
 877            end_on_exit (default: True): Whether to end the span automatically when leaving the context manager. If False, the span must be manually ended to avoid memory leaks.
 878
 879        Returns:
 880            A context manager that yields a LangfuseGeneration
 881
 882        Example:
 883            ```python
 884            with langfuse.start_as_current_generation(
 885                name="answer-generation",
 886                model="gpt-4",
 887                input={"prompt": "Explain quantum computing"}
 888            ) as generation:
 889                # Call model API
 890                response = llm.generate(...)
 891
 892                # Update with results
 893                generation.update(
 894                    output=response.text,
 895                    usage_details={
 896                        "prompt_tokens": response.usage.prompt_tokens,
 897                        "completion_tokens": response.usage.completion_tokens
 898                    }
 899                )
 900            ```
 901        """
 902        warnings.warn(
 903            "start_as_current_generation is deprecated and will be removed in a future version. "
 904            "Use start_as_current_observation(as_type='generation') instead.",
 905            DeprecationWarning,
 906            stacklevel=2,
 907        )
 908        return self.start_as_current_observation(
 909            trace_context=trace_context,
 910            name=name,
 911            as_type="generation",
 912            input=input,
 913            output=output,
 914            metadata=metadata,
 915            version=version,
 916            level=level,
 917            status_message=status_message,
 918            completion_start_time=completion_start_time,
 919            model=model,
 920            model_parameters=model_parameters,
 921            usage_details=usage_details,
 922            cost_details=cost_details,
 923            prompt=prompt,
 924            end_on_exit=end_on_exit,
 925        )
 926
 927    @overload
 928    def start_as_current_observation(
 929        self,
 930        *,
 931        trace_context: Optional[TraceContext] = None,
 932        name: str,
 933        as_type: Literal["generation"],
 934        input: Optional[Any] = None,
 935        output: Optional[Any] = None,
 936        metadata: Optional[Any] = None,
 937        version: Optional[str] = None,
 938        level: Optional[SpanLevel] = None,
 939        status_message: Optional[str] = None,
 940        completion_start_time: Optional[datetime] = None,
 941        model: Optional[str] = None,
 942        model_parameters: Optional[Dict[str, MapValue]] = None,
 943        usage_details: Optional[Dict[str, int]] = None,
 944        cost_details: Optional[Dict[str, float]] = None,
 945        prompt: Optional[PromptClient] = None,
 946        end_on_exit: Optional[bool] = None,
 947    ) -> _AgnosticContextManager[LangfuseGeneration]: ...
 948
 949    @overload
 950    def start_as_current_observation(
 951        self,
 952        *,
 953        trace_context: Optional[TraceContext] = None,
 954        name: str,
 955        as_type: Literal["span"] = "span",
 956        input: Optional[Any] = None,
 957        output: Optional[Any] = None,
 958        metadata: Optional[Any] = None,
 959        version: Optional[str] = None,
 960        level: Optional[SpanLevel] = None,
 961        status_message: Optional[str] = None,
 962        end_on_exit: Optional[bool] = None,
 963    ) -> _AgnosticContextManager[LangfuseSpan]: ...
 964
 965    @overload
 966    def start_as_current_observation(
 967        self,
 968        *,
 969        trace_context: Optional[TraceContext] = None,
 970        name: str,
 971        as_type: Literal["agent"],
 972        input: Optional[Any] = None,
 973        output: Optional[Any] = None,
 974        metadata: Optional[Any] = None,
 975        version: Optional[str] = None,
 976        level: Optional[SpanLevel] = None,
 977        status_message: Optional[str] = None,
 978        end_on_exit: Optional[bool] = None,
 979    ) -> _AgnosticContextManager[LangfuseAgent]: ...
 980
 981    @overload
 982    def start_as_current_observation(
 983        self,
 984        *,
 985        trace_context: Optional[TraceContext] = None,
 986        name: str,
 987        as_type: Literal["tool"],
 988        input: Optional[Any] = None,
 989        output: Optional[Any] = None,
 990        metadata: Optional[Any] = None,
 991        version: Optional[str] = None,
 992        level: Optional[SpanLevel] = None,
 993        status_message: Optional[str] = None,
 994        end_on_exit: Optional[bool] = None,
 995    ) -> _AgnosticContextManager[LangfuseTool]: ...
 996
 997    @overload
 998    def start_as_current_observation(
 999        self,
1000        *,
1001        trace_context: Optional[TraceContext] = None,
1002        name: str,
1003        as_type: Literal["chain"],
1004        input: Optional[Any] = None,
1005        output: Optional[Any] = None,
1006        metadata: Optional[Any] = None,
1007        version: Optional[str] = None,
1008        level: Optional[SpanLevel] = None,
1009        status_message: Optional[str] = None,
1010        end_on_exit: Optional[bool] = None,
1011    ) -> _AgnosticContextManager[LangfuseChain]: ...
1012
1013    @overload
1014    def start_as_current_observation(
1015        self,
1016        *,
1017        trace_context: Optional[TraceContext] = None,
1018        name: str,
1019        as_type: Literal["retriever"],
1020        input: Optional[Any] = None,
1021        output: Optional[Any] = None,
1022        metadata: Optional[Any] = None,
1023        version: Optional[str] = None,
1024        level: Optional[SpanLevel] = None,
1025        status_message: Optional[str] = None,
1026        end_on_exit: Optional[bool] = None,
1027    ) -> _AgnosticContextManager[LangfuseRetriever]: ...
1028
1029    @overload
1030    def start_as_current_observation(
1031        self,
1032        *,
1033        trace_context: Optional[TraceContext] = None,
1034        name: str,
1035        as_type: Literal["evaluator"],
1036        input: Optional[Any] = None,
1037        output: Optional[Any] = None,
1038        metadata: Optional[Any] = None,
1039        version: Optional[str] = None,
1040        level: Optional[SpanLevel] = None,
1041        status_message: Optional[str] = None,
1042        end_on_exit: Optional[bool] = None,
1043    ) -> _AgnosticContextManager[LangfuseEvaluator]: ...
1044
1045    @overload
1046    def start_as_current_observation(
1047        self,
1048        *,
1049        trace_context: Optional[TraceContext] = None,
1050        name: str,
1051        as_type: Literal["embedding"],
1052        input: Optional[Any] = None,
1053        output: Optional[Any] = None,
1054        metadata: Optional[Any] = None,
1055        version: Optional[str] = None,
1056        level: Optional[SpanLevel] = None,
1057        status_message: Optional[str] = None,
1058        completion_start_time: Optional[datetime] = None,
1059        model: Optional[str] = None,
1060        model_parameters: Optional[Dict[str, MapValue]] = None,
1061        usage_details: Optional[Dict[str, int]] = None,
1062        cost_details: Optional[Dict[str, float]] = None,
1063        prompt: Optional[PromptClient] = None,
1064        end_on_exit: Optional[bool] = None,
1065    ) -> _AgnosticContextManager[LangfuseEmbedding]: ...
1066
1067    @overload
1068    def start_as_current_observation(
1069        self,
1070        *,
1071        trace_context: Optional[TraceContext] = None,
1072        name: str,
1073        as_type: Literal["guardrail"],
1074        input: Optional[Any] = None,
1075        output: Optional[Any] = None,
1076        metadata: Optional[Any] = None,
1077        version: Optional[str] = None,
1078        level: Optional[SpanLevel] = None,
1079        status_message: Optional[str] = None,
1080        end_on_exit: Optional[bool] = None,
1081    ) -> _AgnosticContextManager[LangfuseGuardrail]: ...
1082
1083    def start_as_current_observation(
1084        self,
1085        *,
1086        trace_context: Optional[TraceContext] = None,
1087        name: str,
1088        as_type: ObservationTypeLiteralNoEvent = "span",
1089        input: Optional[Any] = None,
1090        output: Optional[Any] = None,
1091        metadata: Optional[Any] = None,
1092        version: Optional[str] = None,
1093        level: Optional[SpanLevel] = None,
1094        status_message: Optional[str] = None,
1095        completion_start_time: Optional[datetime] = None,
1096        model: Optional[str] = None,
1097        model_parameters: Optional[Dict[str, MapValue]] = None,
1098        usage_details: Optional[Dict[str, int]] = None,
1099        cost_details: Optional[Dict[str, float]] = None,
1100        prompt: Optional[PromptClient] = None,
1101        end_on_exit: Optional[bool] = None,
1102    ) -> Union[
1103        _AgnosticContextManager[LangfuseGeneration],
1104        _AgnosticContextManager[LangfuseSpan],
1105        _AgnosticContextManager[LangfuseAgent],
1106        _AgnosticContextManager[LangfuseTool],
1107        _AgnosticContextManager[LangfuseChain],
1108        _AgnosticContextManager[LangfuseRetriever],
1109        _AgnosticContextManager[LangfuseEvaluator],
1110        _AgnosticContextManager[LangfuseEmbedding],
1111        _AgnosticContextManager[LangfuseGuardrail],
1112    ]:
1113        """Create a new observation and set it as the current span in a context manager.
1114
1115        This method creates a new observation of the specified type and sets it as the
1116        current span within a context manager. Use this method with a 'with' statement to
1117        automatically handle the observation lifecycle within a code block.
1118
1119        The created observation will be the child of the current span in the context.
1120
1121        Args:
1122            trace_context: Optional context for connecting to an existing trace
1123            name: Name of the observation (e.g., function or operation name)
1124            as_type: Type of observation to create (defaults to "span")
1125            input: Input data for the operation (can be any JSON-serializable object)
1126            output: Output data from the operation (can be any JSON-serializable object)
1127            metadata: Additional metadata to associate with the observation
1128            version: Version identifier for the code or component
1129            level: Importance level of the observation (info, warning, error)
1130            status_message: Optional status message for the observation
1131            end_on_exit (default: True): Whether to end the span automatically when leaving the context manager. If False, the span must be manually ended to avoid memory leaks.
1132
1133            The following parameters are available when as_type is: "generation" or "embedding".
1134            completion_start_time: When the model started generating the response
1135            model: Name/identifier of the AI model used (e.g., "gpt-4")
1136            model_parameters: Parameters used for the model (e.g., temperature, max_tokens)
1137            usage_details: Token usage information (e.g., prompt_tokens, completion_tokens)
1138            cost_details: Cost information for the model call
1139            prompt: Associated prompt template from Langfuse prompt management
1140
1141        Returns:
1142            A context manager that yields the appropriate observation type based on as_type
1143
1144        Example:
1145            ```python
1146            # Create a span
1147            with langfuse.start_as_current_observation(name="process-query", as_type="span") as span:
1148                # Do work
1149                result = process_data()
1150                span.update(output=result)
1151
1152                # Create a child span automatically
1153                with span.start_as_current_span(name="sub-operation") as child_span:
1154                    # Do sub-operation work
1155                    child_span.update(output="sub-result")
1156
1157            # Create a tool observation
1158            with langfuse.start_as_current_observation(name="web-search", as_type="tool") as tool:
1159                # Do tool work
1160                results = search_web(query)
1161                tool.update(output=results)
1162
1163            # Create a generation observation
1164            with langfuse.start_as_current_observation(
1165                name="answer-generation",
1166                as_type="generation",
1167                model="gpt-4"
1168            ) as generation:
1169                # Generate answer
1170                response = llm.generate(...)
1171                generation.update(output=response)
1172            ```
1173        """
1174        if as_type in get_observation_types_list(ObservationTypeGenerationLike):
1175            if trace_context:
1176                trace_id = trace_context.get("trace_id", None)
1177                parent_span_id = trace_context.get("parent_span_id", None)
1178
1179                if trace_id:
1180                    remote_parent_span = self._create_remote_parent_span(
1181                        trace_id=trace_id, parent_span_id=parent_span_id
1182                    )
1183
1184                    return cast(
1185                        Union[
1186                            _AgnosticContextManager[LangfuseGeneration],
1187                            _AgnosticContextManager[LangfuseEmbedding],
1188                        ],
1189                        self._create_span_with_parent_context(
1190                            as_type=as_type,
1191                            name=name,
1192                            remote_parent_span=remote_parent_span,
1193                            parent=None,
1194                            end_on_exit=end_on_exit,
1195                            input=input,
1196                            output=output,
1197                            metadata=metadata,
1198                            version=version,
1199                            level=level,
1200                            status_message=status_message,
1201                            completion_start_time=completion_start_time,
1202                            model=model,
1203                            model_parameters=model_parameters,
1204                            usage_details=usage_details,
1205                            cost_details=cost_details,
1206                            prompt=prompt,
1207                        ),
1208                    )
1209
1210            return cast(
1211                Union[
1212                    _AgnosticContextManager[LangfuseGeneration],
1213                    _AgnosticContextManager[LangfuseEmbedding],
1214                ],
1215                self._start_as_current_otel_span_with_processed_media(
1216                    as_type=as_type,
1217                    name=name,
1218                    end_on_exit=end_on_exit,
1219                    input=input,
1220                    output=output,
1221                    metadata=metadata,
1222                    version=version,
1223                    level=level,
1224                    status_message=status_message,
1225                    completion_start_time=completion_start_time,
1226                    model=model,
1227                    model_parameters=model_parameters,
1228                    usage_details=usage_details,
1229                    cost_details=cost_details,
1230                    prompt=prompt,
1231                ),
1232            )
1233
1234        if as_type in get_observation_types_list(ObservationTypeSpanLike):
1235            if trace_context:
1236                trace_id = trace_context.get("trace_id", None)
1237                parent_span_id = trace_context.get("parent_span_id", None)
1238
1239                if trace_id:
1240                    remote_parent_span = self._create_remote_parent_span(
1241                        trace_id=trace_id, parent_span_id=parent_span_id
1242                    )
1243
1244                    return cast(
1245                        Union[
1246                            _AgnosticContextManager[LangfuseSpan],
1247                            _AgnosticContextManager[LangfuseAgent],
1248                            _AgnosticContextManager[LangfuseTool],
1249                            _AgnosticContextManager[LangfuseChain],
1250                            _AgnosticContextManager[LangfuseRetriever],
1251                            _AgnosticContextManager[LangfuseEvaluator],
1252                            _AgnosticContextManager[LangfuseGuardrail],
1253                        ],
1254                        self._create_span_with_parent_context(
1255                            as_type=as_type,
1256                            name=name,
1257                            remote_parent_span=remote_parent_span,
1258                            parent=None,
1259                            end_on_exit=end_on_exit,
1260                            input=input,
1261                            output=output,
1262                            metadata=metadata,
1263                            version=version,
1264                            level=level,
1265                            status_message=status_message,
1266                        ),
1267                    )
1268
1269            return cast(
1270                Union[
1271                    _AgnosticContextManager[LangfuseSpan],
1272                    _AgnosticContextManager[LangfuseAgent],
1273                    _AgnosticContextManager[LangfuseTool],
1274                    _AgnosticContextManager[LangfuseChain],
1275                    _AgnosticContextManager[LangfuseRetriever],
1276                    _AgnosticContextManager[LangfuseEvaluator],
1277                    _AgnosticContextManager[LangfuseGuardrail],
1278                ],
1279                self._start_as_current_otel_span_with_processed_media(
1280                    as_type=as_type,
1281                    name=name,
1282                    end_on_exit=end_on_exit,
1283                    input=input,
1284                    output=output,
1285                    metadata=metadata,
1286                    version=version,
1287                    level=level,
1288                    status_message=status_message,
1289                ),
1290            )
1291
1292        # This should never be reached since all valid types are handled above
1293        langfuse_logger.warning(
1294            f"Unknown observation type: {as_type}, falling back to span"
1295        )
1296        return self._start_as_current_otel_span_with_processed_media(
1297            as_type="span",
1298            name=name,
1299            end_on_exit=end_on_exit,
1300            input=input,
1301            output=output,
1302            metadata=metadata,
1303            version=version,
1304            level=level,
1305            status_message=status_message,
1306        )
1307
1308    def _get_span_class(
1309        self,
1310        as_type: ObservationTypeLiteral,
1311    ) -> Union[
1312        Type[LangfuseAgent],
1313        Type[LangfuseTool],
1314        Type[LangfuseChain],
1315        Type[LangfuseRetriever],
1316        Type[LangfuseEvaluator],
1317        Type[LangfuseEmbedding],
1318        Type[LangfuseGuardrail],
1319        Type[LangfuseGeneration],
1320        Type[LangfuseEvent],
1321        Type[LangfuseSpan],
1322    ]:
1323        """Get the appropriate span class based on as_type."""
1324        normalized_type = as_type.lower()
1325
1326        if normalized_type == "agent":
1327            return LangfuseAgent
1328        elif normalized_type == "tool":
1329            return LangfuseTool
1330        elif normalized_type == "chain":
1331            return LangfuseChain
1332        elif normalized_type == "retriever":
1333            return LangfuseRetriever
1334        elif normalized_type == "evaluator":
1335            return LangfuseEvaluator
1336        elif normalized_type == "embedding":
1337            return LangfuseEmbedding
1338        elif normalized_type == "guardrail":
1339            return LangfuseGuardrail
1340        elif normalized_type == "generation":
1341            return LangfuseGeneration
1342        elif normalized_type == "event":
1343            return LangfuseEvent
1344        elif normalized_type == "span":
1345            return LangfuseSpan
1346        else:
1347            return LangfuseSpan
1348
1349    @_agnosticcontextmanager
1350    def _create_span_with_parent_context(
1351        self,
1352        *,
1353        name: str,
1354        parent: Optional[otel_trace_api.Span] = None,
1355        remote_parent_span: Optional[otel_trace_api.Span] = None,
1356        as_type: ObservationTypeLiteralNoEvent,
1357        end_on_exit: Optional[bool] = None,
1358        input: Optional[Any] = None,
1359        output: Optional[Any] = None,
1360        metadata: Optional[Any] = None,
1361        version: Optional[str] = None,
1362        level: Optional[SpanLevel] = None,
1363        status_message: Optional[str] = None,
1364        completion_start_time: Optional[datetime] = None,
1365        model: Optional[str] = None,
1366        model_parameters: Optional[Dict[str, MapValue]] = None,
1367        usage_details: Optional[Dict[str, int]] = None,
1368        cost_details: Optional[Dict[str, float]] = None,
1369        prompt: Optional[PromptClient] = None,
1370    ) -> Any:
1371        parent_span = parent or cast(otel_trace_api.Span, remote_parent_span)
1372
1373        with otel_trace_api.use_span(parent_span):
1374            with self._start_as_current_otel_span_with_processed_media(
1375                name=name,
1376                as_type=as_type,
1377                end_on_exit=end_on_exit,
1378                input=input,
1379                output=output,
1380                metadata=metadata,
1381                version=version,
1382                level=level,
1383                status_message=status_message,
1384                completion_start_time=completion_start_time,
1385                model=model,
1386                model_parameters=model_parameters,
1387                usage_details=usage_details,
1388                cost_details=cost_details,
1389                prompt=prompt,
1390            ) as langfuse_span:
1391                if remote_parent_span is not None:
1392                    langfuse_span._otel_span.set_attribute(
1393                        LangfuseOtelSpanAttributes.AS_ROOT, True
1394                    )
1395
1396                yield langfuse_span
1397
1398    @_agnosticcontextmanager
1399    def _start_as_current_otel_span_with_processed_media(
1400        self,
1401        *,
1402        name: str,
1403        as_type: Optional[ObservationTypeLiteralNoEvent] = None,
1404        end_on_exit: Optional[bool] = None,
1405        input: Optional[Any] = None,
1406        output: Optional[Any] = None,
1407        metadata: Optional[Any] = None,
1408        version: Optional[str] = None,
1409        level: Optional[SpanLevel] = None,
1410        status_message: Optional[str] = None,
1411        completion_start_time: Optional[datetime] = None,
1412        model: Optional[str] = None,
1413        model_parameters: Optional[Dict[str, MapValue]] = None,
1414        usage_details: Optional[Dict[str, int]] = None,
1415        cost_details: Optional[Dict[str, float]] = None,
1416        prompt: Optional[PromptClient] = None,
1417    ) -> Any:
1418        with self._otel_tracer.start_as_current_span(
1419            name=name,
1420            end_on_exit=end_on_exit if end_on_exit is not None else True,
1421        ) as otel_span:
1422            span_class = self._get_span_class(
1423                as_type or "generation"
1424            )  # default was "generation"
1425            common_args = {
1426                "otel_span": otel_span,
1427                "langfuse_client": self,
1428                "environment": self._environment,
1429                "input": input,
1430                "output": output,
1431                "metadata": metadata,
1432                "version": version,
1433                "level": level,
1434                "status_message": status_message,
1435            }
1436
1437            if span_class in [
1438                LangfuseGeneration,
1439                LangfuseEmbedding,
1440            ]:
1441                common_args.update(
1442                    {
1443                        "completion_start_time": completion_start_time,
1444                        "model": model,
1445                        "model_parameters": model_parameters,
1446                        "usage_details": usage_details,
1447                        "cost_details": cost_details,
1448                        "prompt": prompt,
1449                    }
1450                )
1451            # For span-like types (span, agent, tool, chain, retriever, evaluator, guardrail), no generation properties needed
1452
1453            yield span_class(**common_args)  # type: ignore[arg-type]
1454
1455    def _get_current_otel_span(self) -> Optional[otel_trace_api.Span]:
1456        current_span = otel_trace_api.get_current_span()
1457
1458        if current_span is otel_trace_api.INVALID_SPAN:
1459            langfuse_logger.warning(
1460                "Context error: No active span in current context. Operations that depend on an active span will be skipped. "
1461                "Ensure spans are created with start_as_current_span() or that you're operating within an active span context."
1462            )
1463            return None
1464
1465        return current_span
1466
1467    def update_current_generation(
1468        self,
1469        *,
1470        name: Optional[str] = None,
1471        input: Optional[Any] = None,
1472        output: Optional[Any] = None,
1473        metadata: Optional[Any] = None,
1474        version: Optional[str] = None,
1475        level: Optional[SpanLevel] = None,
1476        status_message: Optional[str] = None,
1477        completion_start_time: Optional[datetime] = None,
1478        model: Optional[str] = None,
1479        model_parameters: Optional[Dict[str, MapValue]] = None,
1480        usage_details: Optional[Dict[str, int]] = None,
1481        cost_details: Optional[Dict[str, float]] = None,
1482        prompt: Optional[PromptClient] = None,
1483    ) -> None:
1484        """Update the current active generation span with new information.
1485
1486        This method updates the current generation span in the active context with
1487        additional information. It's useful for adding output, usage stats, or other
1488        details that become available during or after model generation.
1489
1490        Args:
1491            name: The generation name
1492            input: Updated input data for the model
1493            output: Output from the model (e.g., completions)
1494            metadata: Additional metadata to associate with the generation
1495            version: Version identifier for the model or component
1496            level: Importance level of the generation (info, warning, error)
1497            status_message: Optional status message for the generation
1498            completion_start_time: When the model started generating the response
1499            model: Name/identifier of the AI model used (e.g., "gpt-4")
1500            model_parameters: Parameters used for the model (e.g., temperature, max_tokens)
1501            usage_details: Token usage information (e.g., prompt_tokens, completion_tokens)
1502            cost_details: Cost information for the model call
1503            prompt: Associated prompt template from Langfuse prompt management
1504
1505        Example:
1506            ```python
1507            with langfuse.start_as_current_generation(name="answer-query") as generation:
1508                # Initial setup and API call
1509                response = llm.generate(...)
1510
1511                # Update with results that weren't available at creation time
1512                langfuse.update_current_generation(
1513                    output=response.text,
1514                    usage_details={
1515                        "prompt_tokens": response.usage.prompt_tokens,
1516                        "completion_tokens": response.usage.completion_tokens
1517                    }
1518                )
1519            ```
1520        """
1521        if not self._tracing_enabled:
1522            langfuse_logger.debug(
1523                "Operation skipped: update_current_generation - Tracing is disabled or client is in no-op mode."
1524            )
1525            return
1526
1527        current_otel_span = self._get_current_otel_span()
1528
1529        if current_otel_span is not None:
1530            generation = LangfuseGeneration(
1531                otel_span=current_otel_span, langfuse_client=self
1532            )
1533
1534            if name:
1535                current_otel_span.update_name(name)
1536
1537            generation.update(
1538                input=input,
1539                output=output,
1540                metadata=metadata,
1541                version=version,
1542                level=level,
1543                status_message=status_message,
1544                completion_start_time=completion_start_time,
1545                model=model,
1546                model_parameters=model_parameters,
1547                usage_details=usage_details,
1548                cost_details=cost_details,
1549                prompt=prompt,
1550            )
1551
1552    def update_current_span(
1553        self,
1554        *,
1555        name: Optional[str] = None,
1556        input: Optional[Any] = None,
1557        output: Optional[Any] = None,
1558        metadata: Optional[Any] = None,
1559        version: Optional[str] = None,
1560        level: Optional[SpanLevel] = None,
1561        status_message: Optional[str] = None,
1562    ) -> None:
1563        """Update the current active span with new information.
1564
1565        This method updates the current span in the active context with
1566        additional information. It's useful for adding outputs or metadata
1567        that become available during execution.
1568
1569        Args:
1570            name: The span name
1571            input: Updated input data for the operation
1572            output: Output data from the operation
1573            metadata: Additional metadata to associate with the span
1574            version: Version identifier for the code or component
1575            level: Importance level of the span (info, warning, error)
1576            status_message: Optional status message for the span
1577
1578        Example:
1579            ```python
1580            with langfuse.start_as_current_span(name="process-data") as span:
1581                # Initial processing
1582                result = process_first_part()
1583
1584                # Update with intermediate results
1585                langfuse.update_current_span(metadata={"intermediate_result": result})
1586
1587                # Continue processing
1588                final_result = process_second_part(result)
1589
1590                # Final update
1591                langfuse.update_current_span(output=final_result)
1592            ```
1593        """
1594        if not self._tracing_enabled:
1595            langfuse_logger.debug(
1596                "Operation skipped: update_current_span - Tracing is disabled or client is in no-op mode."
1597            )
1598            return
1599
1600        current_otel_span = self._get_current_otel_span()
1601
1602        if current_otel_span is not None:
1603            span = LangfuseSpan(
1604                otel_span=current_otel_span,
1605                langfuse_client=self,
1606                environment=self._environment,
1607            )
1608
1609            if name:
1610                current_otel_span.update_name(name)
1611
1612            span.update(
1613                input=input,
1614                output=output,
1615                metadata=metadata,
1616                version=version,
1617                level=level,
1618                status_message=status_message,
1619            )
1620
1621    def update_current_trace(
1622        self,
1623        *,
1624        name: Optional[str] = None,
1625        user_id: Optional[str] = None,
1626        session_id: Optional[str] = None,
1627        version: Optional[str] = None,
1628        input: Optional[Any] = None,
1629        output: Optional[Any] = None,
1630        metadata: Optional[Any] = None,
1631        tags: Optional[List[str]] = None,
1632        public: Optional[bool] = None,
1633    ) -> None:
1634        """Update the current trace with additional information.
1635
1636        This method updates the Langfuse trace that the current span belongs to. It's useful for
1637        adding trace-level metadata like user ID, session ID, or tags that apply to
1638        the entire Langfuse trace rather than just a single observation.
1639
1640        Args:
1641            name: Updated name for the Langfuse trace
1642            user_id: ID of the user who initiated the Langfuse trace
1643            session_id: Session identifier for grouping related Langfuse traces
1644            version: Version identifier for the application or service
1645            input: Input data for the overall Langfuse trace
1646            output: Output data from the overall Langfuse trace
1647            metadata: Additional metadata to associate with the Langfuse trace
1648            tags: List of tags to categorize the Langfuse trace
1649            public: Whether the Langfuse trace should be publicly accessible
1650
1651        Example:
1652            ```python
1653            with langfuse.start_as_current_span(name="handle-request") as span:
1654                # Get user information
1655                user = authenticate_user(request)
1656
1657                # Update trace with user context
1658                langfuse.update_current_trace(
1659                    user_id=user.id,
1660                    session_id=request.session_id,
1661                    tags=["production", "web-app"]
1662                )
1663
1664                # Continue processing
1665                response = process_request(request)
1666
1667                # Update span with results
1668                span.update(output=response)
1669            ```
1670        """
1671        if not self._tracing_enabled:
1672            langfuse_logger.debug(
1673                "Operation skipped: update_current_trace - Tracing is disabled or client is in no-op mode."
1674            )
1675            return
1676
1677        current_otel_span = self._get_current_otel_span()
1678
1679        if current_otel_span is not None:
1680            existing_observation_type = current_otel_span.attributes.get(  # type: ignore[attr-defined]
1681                LangfuseOtelSpanAttributes.OBSERVATION_TYPE, "span"
1682            )
1683            # We need to preserve the class to keep the correct observation type
1684            span_class = self._get_span_class(existing_observation_type)
1685            span = span_class(
1686                otel_span=current_otel_span,
1687                langfuse_client=self,
1688                environment=self._environment,
1689            )
1690
1691            span.update_trace(
1692                name=name,
1693                user_id=user_id,
1694                session_id=session_id,
1695                version=version,
1696                input=input,
1697                output=output,
1698                metadata=metadata,
1699                tags=tags,
1700                public=public,
1701            )
1702
1703    def create_event(
1704        self,
1705        *,
1706        trace_context: Optional[TraceContext] = None,
1707        name: str,
1708        input: Optional[Any] = None,
1709        output: Optional[Any] = None,
1710        metadata: Optional[Any] = None,
1711        version: Optional[str] = None,
1712        level: Optional[SpanLevel] = None,
1713        status_message: Optional[str] = None,
1714    ) -> LangfuseEvent:
1715        """Create a new Langfuse observation of type 'EVENT'.
1716
1717        The created Langfuse Event observation will be the child of the current span in the context.
1718
1719        Args:
1720            trace_context: Optional context for connecting to an existing trace
1721            name: Name of the span (e.g., function or operation name)
1722            input: Input data for the operation (can be any JSON-serializable object)
1723            output: Output data from the operation (can be any JSON-serializable object)
1724            metadata: Additional metadata to associate with the span
1725            version: Version identifier for the code or component
1726            level: Importance level of the span (info, warning, error)
1727            status_message: Optional status message for the span
1728
1729        Returns:
1730            The Langfuse Event object
1731
1732        Example:
1733            ```python
1734            event = langfuse.create_event(name="process-event")
1735            ```
1736        """
1737        timestamp = time_ns()
1738
1739        if trace_context:
1740            trace_id = trace_context.get("trace_id", None)
1741            parent_span_id = trace_context.get("parent_span_id", None)
1742
1743            if trace_id:
1744                remote_parent_span = self._create_remote_parent_span(
1745                    trace_id=trace_id, parent_span_id=parent_span_id
1746                )
1747
1748                with otel_trace_api.use_span(
1749                    cast(otel_trace_api.Span, remote_parent_span)
1750                ):
1751                    otel_span = self._otel_tracer.start_span(
1752                        name=name, start_time=timestamp
1753                    )
1754                    otel_span.set_attribute(LangfuseOtelSpanAttributes.AS_ROOT, True)
1755
1756                    return cast(
1757                        LangfuseEvent,
1758                        LangfuseEvent(
1759                            otel_span=otel_span,
1760                            langfuse_client=self,
1761                            environment=self._environment,
1762                            input=input,
1763                            output=output,
1764                            metadata=metadata,
1765                            version=version,
1766                            level=level,
1767                            status_message=status_message,
1768                        ).end(end_time=timestamp),
1769                    )
1770
1771        otel_span = self._otel_tracer.start_span(name=name, start_time=timestamp)
1772
1773        return cast(
1774            LangfuseEvent,
1775            LangfuseEvent(
1776                otel_span=otel_span,
1777                langfuse_client=self,
1778                environment=self._environment,
1779                input=input,
1780                output=output,
1781                metadata=metadata,
1782                version=version,
1783                level=level,
1784                status_message=status_message,
1785            ).end(end_time=timestamp),
1786        )
1787
1788    def _create_remote_parent_span(
1789        self, *, trace_id: str, parent_span_id: Optional[str]
1790    ) -> Any:
1791        if not self._is_valid_trace_id(trace_id):
1792            langfuse_logger.warning(
1793                f"Passed trace ID '{trace_id}' is not a valid 32 lowercase hex char Langfuse trace id. Ignoring trace ID."
1794            )
1795
1796        if parent_span_id and not self._is_valid_span_id(parent_span_id):
1797            langfuse_logger.warning(
1798                f"Passed span ID '{parent_span_id}' is not a valid 16 lowercase hex char Langfuse span id. Ignoring parent span ID."
1799            )
1800
1801        int_trace_id = int(trace_id, 16)
1802        int_parent_span_id = (
1803            int(parent_span_id, 16)
1804            if parent_span_id
1805            else RandomIdGenerator().generate_span_id()
1806        )
1807
1808        span_context = otel_trace_api.SpanContext(
1809            trace_id=int_trace_id,
1810            span_id=int_parent_span_id,
1811            trace_flags=otel_trace_api.TraceFlags(0x01),  # mark span as sampled
1812            is_remote=False,
1813        )
1814
1815        return trace.NonRecordingSpan(span_context)
1816
1817    def _is_valid_trace_id(self, trace_id: str) -> bool:
1818        pattern = r"^[0-9a-f]{32}$"
1819
1820        return bool(re.match(pattern, trace_id))
1821
1822    def _is_valid_span_id(self, span_id: str) -> bool:
1823        pattern = r"^[0-9a-f]{16}$"
1824
1825        return bool(re.match(pattern, span_id))
1826
1827    def _create_observation_id(self, *, seed: Optional[str] = None) -> str:
1828        """Create a unique observation ID for use with Langfuse.
1829
1830        This method generates a unique observation ID (span ID in OpenTelemetry terms)
1831        for use with various Langfuse APIs. It can either generate a random ID or
1832        create a deterministic ID based on a seed string.
1833
1834        Observation IDs must be 16 lowercase hexadecimal characters, representing 8 bytes.
1835        This method ensures the generated ID meets this requirement. If you need to
1836        correlate an external ID with a Langfuse observation ID, use the external ID as
1837        the seed to get a valid, deterministic observation ID.
1838
1839        Args:
1840            seed: Optional string to use as a seed for deterministic ID generation.
1841                 If provided, the same seed will always produce the same ID.
1842                 If not provided, a random ID will be generated.
1843
1844        Returns:
1845            A 16-character lowercase hexadecimal string representing the observation ID.
1846
1847        Example:
1848            ```python
1849            # Generate a random observation ID
1850            obs_id = langfuse.create_observation_id()
1851
1852            # Generate a deterministic ID based on a seed
1853            user_obs_id = langfuse.create_observation_id(seed="user-123-feedback")
1854
1855            # Correlate an external item ID with a Langfuse observation ID
1856            item_id = "item-789012"
1857            correlated_obs_id = langfuse.create_observation_id(seed=item_id)
1858
1859            # Use the ID with Langfuse APIs
1860            langfuse.create_score(
1861                name="relevance",
1862                value=0.95,
1863                trace_id=trace_id,
1864                observation_id=obs_id
1865            )
1866            ```
1867        """
1868        if not seed:
1869            span_id_int = RandomIdGenerator().generate_span_id()
1870
1871            return self._format_otel_span_id(span_id_int)
1872
1873        return sha256(seed.encode("utf-8")).digest()[:8].hex()
1874
1875    @staticmethod
1876    def create_trace_id(*, seed: Optional[str] = None) -> str:
1877        """Create a unique trace ID for use with Langfuse.
1878
1879        This method generates a unique trace ID for use with various Langfuse APIs.
1880        It can either generate a random ID or create a deterministic ID based on
1881        a seed string.
1882
1883        Trace IDs must be 32 lowercase hexadecimal characters, representing 16 bytes.
1884        This method ensures the generated ID meets this requirement. If you need to
1885        correlate an external ID with a Langfuse trace ID, use the external ID as the
1886        seed to get a valid, deterministic Langfuse trace ID.
1887
1888        Args:
1889            seed: Optional string to use as a seed for deterministic ID generation.
1890                 If provided, the same seed will always produce the same ID.
1891                 If not provided, a random ID will be generated.
1892
1893        Returns:
1894            A 32-character lowercase hexadecimal string representing the Langfuse trace ID.
1895
1896        Example:
1897            ```python
1898            # Generate a random trace ID
1899            trace_id = langfuse.create_trace_id()
1900
1901            # Generate a deterministic ID based on a seed
1902            session_trace_id = langfuse.create_trace_id(seed="session-456")
1903
1904            # Correlate an external ID with a Langfuse trace ID
1905            external_id = "external-system-123456"
1906            correlated_trace_id = langfuse.create_trace_id(seed=external_id)
1907
1908            # Use the ID with trace context
1909            with langfuse.start_as_current_span(
1910                name="process-request",
1911                trace_context={"trace_id": trace_id}
1912            ) as span:
1913                # Operation will be part of the specific trace
1914                pass
1915            ```
1916        """
1917        if not seed:
1918            trace_id_int = RandomIdGenerator().generate_trace_id()
1919
1920            return Langfuse._format_otel_trace_id(trace_id_int)
1921
1922        return sha256(seed.encode("utf-8")).digest()[:16].hex()
1923
1924    def _get_otel_trace_id(self, otel_span: otel_trace_api.Span) -> str:
1925        span_context = otel_span.get_span_context()
1926
1927        return self._format_otel_trace_id(span_context.trace_id)
1928
1929    def _get_otel_span_id(self, otel_span: otel_trace_api.Span) -> str:
1930        span_context = otel_span.get_span_context()
1931
1932        return self._format_otel_span_id(span_context.span_id)
1933
1934    @staticmethod
1935    def _format_otel_span_id(span_id_int: int) -> str:
1936        """Format an integer span ID to a 16-character lowercase hex string.
1937
1938        Internal method to convert an OpenTelemetry integer span ID to the standard
1939        W3C Trace Context format (16-character lowercase hex string).
1940
1941        Args:
1942            span_id_int: 64-bit integer representing a span ID
1943
1944        Returns:
1945            A 16-character lowercase hexadecimal string
1946        """
1947        return format(span_id_int, "016x")
1948
1949    @staticmethod
1950    def _format_otel_trace_id(trace_id_int: int) -> str:
1951        """Format an integer trace ID to a 32-character lowercase hex string.
1952
1953        Internal method to convert an OpenTelemetry integer trace ID to the standard
1954        W3C Trace Context format (32-character lowercase hex string).
1955
1956        Args:
1957            trace_id_int: 128-bit integer representing a trace ID
1958
1959        Returns:
1960            A 32-character lowercase hexadecimal string
1961        """
1962        return format(trace_id_int, "032x")
1963
1964    @overload
1965    def create_score(
1966        self,
1967        *,
1968        name: str,
1969        value: float,
1970        session_id: Optional[str] = None,
1971        dataset_run_id: Optional[str] = None,
1972        trace_id: Optional[str] = None,
1973        observation_id: Optional[str] = None,
1974        score_id: Optional[str] = None,
1975        data_type: Optional[Literal["NUMERIC", "BOOLEAN"]] = None,
1976        comment: Optional[str] = None,
1977        config_id: Optional[str] = None,
1978        metadata: Optional[Any] = None,
1979    ) -> None: ...
1980
1981    @overload
1982    def create_score(
1983        self,
1984        *,
1985        name: str,
1986        value: str,
1987        session_id: Optional[str] = None,
1988        dataset_run_id: Optional[str] = None,
1989        trace_id: Optional[str] = None,
1990        score_id: Optional[str] = None,
1991        observation_id: Optional[str] = None,
1992        data_type: Optional[Literal["CATEGORICAL"]] = "CATEGORICAL",
1993        comment: Optional[str] = None,
1994        config_id: Optional[str] = None,
1995        metadata: Optional[Any] = None,
1996    ) -> None: ...
1997
1998    def create_score(
1999        self,
2000        *,
2001        name: str,
2002        value: Union[float, str],
2003        session_id: Optional[str] = None,
2004        dataset_run_id: Optional[str] = None,
2005        trace_id: Optional[str] = None,
2006        observation_id: Optional[str] = None,
2007        score_id: Optional[str] = None,
2008        data_type: Optional[ScoreDataType] = None,
2009        comment: Optional[str] = None,
2010        config_id: Optional[str] = None,
2011        metadata: Optional[Any] = None,
2012    ) -> None:
2013        """Create a score for a specific trace or observation.
2014
2015        This method creates a score for evaluating a Langfuse trace or observation. Scores can be
2016        used to track quality metrics, user feedback, or automated evaluations.
2017
2018        Args:
2019            name: Name of the score (e.g., "relevance", "accuracy")
2020            value: Score value (can be numeric for NUMERIC/BOOLEAN types or string for CATEGORICAL)
2021            session_id: ID of the Langfuse session to associate the score with
2022            dataset_run_id: ID of the Langfuse dataset run to associate the score with
2023            trace_id: ID of the Langfuse trace to associate the score with
2024            observation_id: Optional ID of the specific observation to score. Trace ID must be provided too.
2025            score_id: Optional custom ID for the score (auto-generated if not provided)
2026            data_type: Type of score (NUMERIC, BOOLEAN, or CATEGORICAL)
2027            comment: Optional comment or explanation for the score
2028            config_id: Optional ID of a score config defined in Langfuse
2029            metadata: Optional metadata to be attached to the score
2030
2031        Example:
2032            ```python
2033            # Create a numeric score for accuracy
2034            langfuse.create_score(
2035                name="accuracy",
2036                value=0.92,
2037                trace_id="abcdef1234567890abcdef1234567890",
2038                data_type="NUMERIC",
2039                comment="High accuracy with minor irrelevant details"
2040            )
2041
2042            # Create a categorical score for sentiment
2043            langfuse.create_score(
2044                name="sentiment",
2045                value="positive",
2046                trace_id="abcdef1234567890abcdef1234567890",
2047                observation_id="abcdef1234567890",
2048                data_type="CATEGORICAL"
2049            )
2050            ```
2051        """
2052        if not self._tracing_enabled:
2053            return
2054
2055        score_id = score_id or self._create_observation_id()
2056
2057        try:
2058            new_body = ScoreBody(
2059                id=score_id,
2060                sessionId=session_id,
2061                datasetRunId=dataset_run_id,
2062                traceId=trace_id,
2063                observationId=observation_id,
2064                name=name,
2065                value=value,
2066                dataType=data_type,  # type: ignore
2067                comment=comment,
2068                configId=config_id,
2069                environment=self._environment,
2070                metadata=metadata,
2071            )
2072
2073            event = {
2074                "id": self.create_trace_id(),
2075                "type": "score-create",
2076                "timestamp": _get_timestamp(),
2077                "body": new_body,
2078            }
2079
2080            if self._resources is not None:
2081                # Force the score to be in sample if it was for a legacy trace ID, i.e. non-32 hexchar
2082                force_sample = (
2083                    not self._is_valid_trace_id(trace_id) if trace_id else True
2084                )
2085
2086                self._resources.add_score_task(
2087                    event,
2088                    force_sample=force_sample,
2089                )
2090
2091        except Exception as e:
2092            langfuse_logger.exception(
2093                f"Error creating score: Failed to process score event for trace_id={trace_id}, name={name}. Error: {e}"
2094            )
2095
2096    @overload
2097    def score_current_span(
2098        self,
2099        *,
2100        name: str,
2101        value: float,
2102        score_id: Optional[str] = None,
2103        data_type: Optional[Literal["NUMERIC", "BOOLEAN"]] = None,
2104        comment: Optional[str] = None,
2105        config_id: Optional[str] = None,
2106    ) -> None: ...
2107
2108    @overload
2109    def score_current_span(
2110        self,
2111        *,
2112        name: str,
2113        value: str,
2114        score_id: Optional[str] = None,
2115        data_type: Optional[Literal["CATEGORICAL"]] = "CATEGORICAL",
2116        comment: Optional[str] = None,
2117        config_id: Optional[str] = None,
2118    ) -> None: ...
2119
2120    def score_current_span(
2121        self,
2122        *,
2123        name: str,
2124        value: Union[float, str],
2125        score_id: Optional[str] = None,
2126        data_type: Optional[ScoreDataType] = None,
2127        comment: Optional[str] = None,
2128        config_id: Optional[str] = None,
2129    ) -> None:
2130        """Create a score for the current active span.
2131
2132        This method scores the currently active span in the context. It's a convenient
2133        way to score the current operation without needing to know its trace and span IDs.
2134
2135        Args:
2136            name: Name of the score (e.g., "relevance", "accuracy")
2137            value: Score value (can be numeric for NUMERIC/BOOLEAN types or string for CATEGORICAL)
2138            score_id: Optional custom ID for the score (auto-generated if not provided)
2139            data_type: Type of score (NUMERIC, BOOLEAN, or CATEGORICAL)
2140            comment: Optional comment or explanation for the score
2141            config_id: Optional ID of a score config defined in Langfuse
2142
2143        Example:
2144            ```python
2145            with langfuse.start_as_current_generation(name="answer-query") as generation:
2146                # Generate answer
2147                response = generate_answer(...)
2148                generation.update(output=response)
2149
2150                # Score the generation
2151                langfuse.score_current_span(
2152                    name="relevance",
2153                    value=0.85,
2154                    data_type="NUMERIC",
2155                    comment="Mostly relevant but contains some tangential information"
2156                )
2157            ```
2158        """
2159        current_span = self._get_current_otel_span()
2160
2161        if current_span is not None:
2162            trace_id = self._get_otel_trace_id(current_span)
2163            observation_id = self._get_otel_span_id(current_span)
2164
2165            langfuse_logger.info(
2166                f"Score: Creating score name='{name}' value={value} for current span ({observation_id}) in trace {trace_id}"
2167            )
2168
2169            self.create_score(
2170                trace_id=trace_id,
2171                observation_id=observation_id,
2172                name=name,
2173                value=cast(str, value),
2174                score_id=score_id,
2175                data_type=cast(Literal["CATEGORICAL"], data_type),
2176                comment=comment,
2177                config_id=config_id,
2178            )
2179
2180    @overload
2181    def score_current_trace(
2182        self,
2183        *,
2184        name: str,
2185        value: float,
2186        score_id: Optional[str] = None,
2187        data_type: Optional[Literal["NUMERIC", "BOOLEAN"]] = None,
2188        comment: Optional[str] = None,
2189        config_id: Optional[str] = None,
2190    ) -> None: ...
2191
2192    @overload
2193    def score_current_trace(
2194        self,
2195        *,
2196        name: str,
2197        value: str,
2198        score_id: Optional[str] = None,
2199        data_type: Optional[Literal["CATEGORICAL"]] = "CATEGORICAL",
2200        comment: Optional[str] = None,
2201        config_id: Optional[str] = None,
2202    ) -> None: ...
2203
2204    def score_current_trace(
2205        self,
2206        *,
2207        name: str,
2208        value: Union[float, str],
2209        score_id: Optional[str] = None,
2210        data_type: Optional[ScoreDataType] = None,
2211        comment: Optional[str] = None,
2212        config_id: Optional[str] = None,
2213    ) -> None:
2214        """Create a score for the current trace.
2215
2216        This method scores the trace of the currently active span. Unlike score_current_span,
2217        this method associates the score with the entire trace rather than a specific span.
2218        It's useful for scoring overall performance or quality of the entire operation.
2219
2220        Args:
2221            name: Name of the score (e.g., "user_satisfaction", "overall_quality")
2222            value: Score value (can be numeric for NUMERIC/BOOLEAN types or string for CATEGORICAL)
2223            score_id: Optional custom ID for the score (auto-generated if not provided)
2224            data_type: Type of score (NUMERIC, BOOLEAN, or CATEGORICAL)
2225            comment: Optional comment or explanation for the score
2226            config_id: Optional ID of a score config defined in Langfuse
2227
2228        Example:
2229            ```python
2230            with langfuse.start_as_current_span(name="process-user-request") as span:
2231                # Process request
2232                result = process_complete_request()
2233                span.update(output=result)
2234
2235                # Score the overall trace
2236                langfuse.score_current_trace(
2237                    name="overall_quality",
2238                    value=0.95,
2239                    data_type="NUMERIC",
2240                    comment="High quality end-to-end response"
2241                )
2242            ```
2243        """
2244        current_span = self._get_current_otel_span()
2245
2246        if current_span is not None:
2247            trace_id = self._get_otel_trace_id(current_span)
2248
2249            langfuse_logger.info(
2250                f"Score: Creating score name='{name}' value={value} for entire trace {trace_id}"
2251            )
2252
2253            self.create_score(
2254                trace_id=trace_id,
2255                name=name,
2256                value=cast(str, value),
2257                score_id=score_id,
2258                data_type=cast(Literal["CATEGORICAL"], data_type),
2259                comment=comment,
2260                config_id=config_id,
2261            )
2262
2263    def flush(self) -> None:
2264        """Force flush all pending spans and events to the Langfuse API.
2265
2266        This method manually flushes any pending spans, scores, and other events to the
2267        Langfuse API. It's useful in scenarios where you want to ensure all data is sent
2268        before proceeding, without waiting for the automatic flush interval.
2269
2270        Example:
2271            ```python
2272            # Record some spans and scores
2273            with langfuse.start_as_current_span(name="operation") as span:
2274                # Do work...
2275                pass
2276
2277            # Ensure all data is sent to Langfuse before proceeding
2278            langfuse.flush()
2279
2280            # Continue with other work
2281            ```
2282        """
2283        if self._resources is not None:
2284            self._resources.flush()
2285
2286    def shutdown(self) -> None:
2287        """Shut down the Langfuse client and flush all pending data.
2288
2289        This method cleanly shuts down the Langfuse client, ensuring all pending data
2290        is flushed to the API and all background threads are properly terminated.
2291
2292        It's important to call this method when your application is shutting down to
2293        prevent data loss and resource leaks. For most applications, using the client
2294        as a context manager or relying on the automatic shutdown via atexit is sufficient.
2295
2296        Example:
2297            ```python
2298            # Initialize Langfuse
2299            langfuse = Langfuse(public_key="...", secret_key="...")
2300
2301            # Use Langfuse throughout your application
2302            # ...
2303
2304            # When application is shutting down
2305            langfuse.shutdown()
2306            ```
2307        """
2308        if self._resources is not None:
2309            self._resources.shutdown()
2310
2311    def get_current_trace_id(self) -> Optional[str]:
2312        """Get the trace ID of the current active span.
2313
2314        This method retrieves the trace ID from the currently active span in the context.
2315        It can be used to get the trace ID for referencing in logs, external systems,
2316        or for creating related operations.
2317
2318        Returns:
2319            The current trace ID as a 32-character lowercase hexadecimal string,
2320            or None if there is no active span.
2321
2322        Example:
2323            ```python
2324            with langfuse.start_as_current_span(name="process-request") as span:
2325                # Get the current trace ID for reference
2326                trace_id = langfuse.get_current_trace_id()
2327
2328                # Use it for external correlation
2329                log.info(f"Processing request with trace_id: {trace_id}")
2330
2331                # Or pass to another system
2332                external_system.process(data, trace_id=trace_id)
2333            ```
2334        """
2335        if not self._tracing_enabled:
2336            langfuse_logger.debug(
2337                "Operation skipped: get_current_trace_id - Tracing is disabled or client is in no-op mode."
2338            )
2339            return None
2340
2341        current_otel_span = self._get_current_otel_span()
2342
2343        return self._get_otel_trace_id(current_otel_span) if current_otel_span else None
2344
2345    def get_current_observation_id(self) -> Optional[str]:
2346        """Get the observation ID (span ID) of the current active span.
2347
2348        This method retrieves the observation ID from the currently active span in the context.
2349        It can be used to get the observation ID for referencing in logs, external systems,
2350        or for creating scores or other related operations.
2351
2352        Returns:
2353            The current observation ID as a 16-character lowercase hexadecimal string,
2354            or None if there is no active span.
2355
2356        Example:
2357            ```python
2358            with langfuse.start_as_current_span(name="process-user-query") as span:
2359                # Get the current observation ID
2360                observation_id = langfuse.get_current_observation_id()
2361
2362                # Store it for later reference
2363                cache.set(f"query_{query_id}_observation", observation_id)
2364
2365                # Process the query...
2366            ```
2367        """
2368        if not self._tracing_enabled:
2369            langfuse_logger.debug(
2370                "Operation skipped: get_current_observation_id - Tracing is disabled or client is in no-op mode."
2371            )
2372            return None
2373
2374        current_otel_span = self._get_current_otel_span()
2375
2376        return self._get_otel_span_id(current_otel_span) if current_otel_span else None
2377
2378    def _get_project_id(self) -> Optional[str]:
2379        """Fetch and return the current project id. Persisted across requests. Returns None if no project id is found for api keys."""
2380        if not self._project_id:
2381            proj = self.api.projects.get()
2382            if not proj.data or not proj.data[0].id:
2383                return None
2384
2385            self._project_id = proj.data[0].id
2386
2387        return self._project_id
2388
2389    def get_trace_url(self, *, trace_id: Optional[str] = None) -> Optional[str]:
2390        """Get the URL to view a trace in the Langfuse UI.
2391
2392        This method generates a URL that links directly to a trace in the Langfuse UI.
2393        It's useful for providing links in logs, notifications, or debugging tools.
2394
2395        Args:
2396            trace_id: Optional trace ID to generate a URL for. If not provided,
2397                     the trace ID of the current active span will be used.
2398
2399        Returns:
2400            A URL string pointing to the trace in the Langfuse UI,
2401            or None if the project ID couldn't be retrieved or no trace ID is available.
2402
2403        Example:
2404            ```python
2405            # Get URL for the current trace
2406            with langfuse.start_as_current_span(name="process-request") as span:
2407                trace_url = langfuse.get_trace_url()
2408                log.info(f"Processing trace: {trace_url}")
2409
2410            # Get URL for a specific trace
2411            specific_trace_url = langfuse.get_trace_url(trace_id="1234567890abcdef1234567890abcdef")
2412            send_notification(f"Review needed for trace: {specific_trace_url}")
2413            ```
2414        """
2415        project_id = self._get_project_id()
2416        final_trace_id = trace_id or self.get_current_trace_id()
2417
2418        return (
2419            f"{self._host}/project/{project_id}/traces/{final_trace_id}"
2420            if project_id and final_trace_id
2421            else None
2422        )
2423
2424    def get_dataset(
2425        self, name: str, *, fetch_items_page_size: Optional[int] = 50
2426    ) -> "DatasetClient":
2427        """Fetch a dataset by its name.
2428
2429        Args:
2430            name (str): The name of the dataset to fetch.
2431            fetch_items_page_size (Optional[int]): All items of the dataset will be fetched in chunks of this size. Defaults to 50.
2432
2433        Returns:
2434            DatasetClient: The dataset with the given name.
2435        """
2436        try:
2437            langfuse_logger.debug(f"Getting datasets {name}")
2438            dataset = self.api.datasets.get(dataset_name=name)
2439
2440            dataset_items = []
2441            page = 1
2442
2443            while True:
2444                new_items = self.api.dataset_items.list(
2445                    dataset_name=self._url_encode(name, is_url_param=True),
2446                    page=page,
2447                    limit=fetch_items_page_size,
2448                )
2449                dataset_items.extend(new_items.data)
2450
2451                if new_items.meta.total_pages <= page:
2452                    break
2453
2454                page += 1
2455
2456            items = [DatasetItemClient(i, langfuse=self) for i in dataset_items]
2457
2458            return DatasetClient(dataset, items=items)
2459
2460        except Error as e:
2461            handle_fern_exception(e)
2462            raise e
2463
2464    def run_experiment(
2465        self,
2466        *,
2467        name: str,
2468        run_name: Optional[str] = None,
2469        description: Optional[str] = None,
2470        data: ExperimentData,
2471        task: TaskFunction,
2472        evaluators: List[EvaluatorFunction] = [],
2473        run_evaluators: List[RunEvaluatorFunction] = [],
2474        max_concurrency: int = 50,
2475        metadata: Optional[Dict[str, Any]] = None,
2476    ) -> ExperimentResult:
2477        """Run an experiment on a dataset with automatic tracing and evaluation.
2478
2479        This method executes a task function on each item in the provided dataset,
2480        automatically traces all executions with Langfuse for observability, runs
2481        item-level and run-level evaluators on the outputs, and returns comprehensive
2482        results with evaluation metrics.
2483
2484        The experiment system provides:
2485        - Automatic tracing of all task executions
2486        - Concurrent processing with configurable limits
2487        - Comprehensive error handling that isolates failures
2488        - Integration with Langfuse datasets for experiment tracking
2489        - Flexible evaluation framework supporting both sync and async evaluators
2490
2491        Args:
2492            name: Human-readable name for the experiment. Used for identification
2493                in the Langfuse UI.
2494            run_name: Optional exact name for the experiment run. If provided, this will be
2495                used as the exact dataset run name if the `data` contains Langfuse dataset items.
2496                If not provided, this will default to the experiment name appended with an ISO timestamp.
2497            description: Optional description explaining the experiment's purpose,
2498                methodology, or expected outcomes.
2499            data: Array of data items to process. Can be either:
2500                - List of dict-like items with 'input', 'expected_output', 'metadata' keys
2501                - List of Langfuse DatasetItem objects from dataset.items
2502            task: Function that processes each data item and returns output.
2503                Must accept 'item' as keyword argument and can return sync or async results.
2504                The task function signature should be: task(*, item, **kwargs) -> Any
2505            evaluators: List of functions to evaluate each item's output individually.
2506                Each evaluator receives input, output, expected_output, and metadata.
2507                Can return single Evaluation dict or list of Evaluation dicts.
2508            run_evaluators: List of functions to evaluate the entire experiment run.
2509                Each run evaluator receives all item_results and can compute aggregate metrics.
2510                Useful for calculating averages, distributions, or cross-item comparisons.
2511            max_concurrency: Maximum number of concurrent task executions (default: 50).
2512                Controls the number of items processed simultaneously. Adjust based on
2513                API rate limits and system resources.
2514            metadata: Optional metadata dictionary to attach to all experiment traces.
2515                This metadata will be included in every trace created during the experiment.
2516                If `data` are Langfuse dataset items, the metadata will be attached to the dataset run, too.
2517
2518        Returns:
2519            ExperimentResult containing:
2520            - run_name: The experiment run name. This is equal to the dataset run name if experiment was on Langfuse dataset.
2521            - item_results: List of results for each processed item with outputs and evaluations
2522            - run_evaluations: List of aggregate evaluation results for the entire run
2523            - dataset_run_id: ID of the dataset run (if using Langfuse datasets)
2524            - dataset_run_url: Direct URL to view results in Langfuse UI (if applicable)
2525
2526        Raises:
2527            ValueError: If required parameters are missing or invalid
2528            Exception: If experiment setup fails (individual item failures are handled gracefully)
2529
2530        Examples:
2531            Basic experiment with local data:
2532            ```python
2533            def summarize_text(*, item, **kwargs):
2534                return f"Summary: {item['input'][:50]}..."
2535
2536            def length_evaluator(*, input, output, expected_output=None, **kwargs):
2537                return {
2538                    "name": "output_length",
2539                    "value": len(output),
2540                    "comment": f"Output contains {len(output)} characters"
2541                }
2542
2543            result = langfuse.run_experiment(
2544                name="Text Summarization Test",
2545                description="Evaluate summarization quality and length",
2546                data=[
2547                    {"input": "Long article text...", "expected_output": "Expected summary"},
2548                    {"input": "Another article...", "expected_output": "Another summary"}
2549                ],
2550                task=summarize_text,
2551                evaluators=[length_evaluator]
2552            )
2553
2554            print(f"Processed {len(result.item_results)} items")
2555            for item_result in result.item_results:
2556                print(f"Input: {item_result.item['input']}")
2557                print(f"Output: {item_result.output}")
2558                print(f"Evaluations: {item_result.evaluations}")
2559            ```
2560
2561            Advanced experiment with async task and multiple evaluators:
2562            ```python
2563            async def llm_task(*, item, **kwargs):
2564                # Simulate async LLM call
2565                response = await openai_client.chat.completions.create(
2566                    model="gpt-4",
2567                    messages=[{"role": "user", "content": item["input"]}]
2568                )
2569                return response.choices[0].message.content
2570
2571            def accuracy_evaluator(*, input, output, expected_output=None, **kwargs):
2572                if expected_output and expected_output.lower() in output.lower():
2573                    return {"name": "accuracy", "value": 1.0, "comment": "Correct answer"}
2574                return {"name": "accuracy", "value": 0.0, "comment": "Incorrect answer"}
2575
2576            def toxicity_evaluator(*, input, output, expected_output=None, **kwargs):
2577                # Simulate toxicity check
2578                toxicity_score = check_toxicity(output)  # Your toxicity checker
2579                return {
2580                    "name": "toxicity",
2581                    "value": toxicity_score,
2582                    "comment": f"Toxicity level: {'high' if toxicity_score > 0.7 else 'low'}"
2583                }
2584
2585            def average_accuracy(*, item_results, **kwargs):
2586                accuracies = [
2587                    eval.value for result in item_results
2588                    for eval in result.evaluations
2589                    if eval.name == "accuracy"
2590                ]
2591                return {
2592                    "name": "average_accuracy",
2593                    "value": sum(accuracies) / len(accuracies) if accuracies else 0,
2594                    "comment": f"Average accuracy across {len(accuracies)} items"
2595                }
2596
2597            result = langfuse.run_experiment(
2598                name="LLM Safety and Accuracy Test",
2599                description="Evaluate model accuracy and safety across diverse prompts",
2600                data=test_dataset,  # Your dataset items
2601                task=llm_task,
2602                evaluators=[accuracy_evaluator, toxicity_evaluator],
2603                run_evaluators=[average_accuracy],
2604                max_concurrency=5,  # Limit concurrent API calls
2605                metadata={"model": "gpt-4", "temperature": 0.7}
2606            )
2607            ```
2608
2609            Using with Langfuse datasets:
2610            ```python
2611            # Get dataset from Langfuse
2612            dataset = langfuse.get_dataset("my-eval-dataset")
2613
2614            result = dataset.run_experiment(
2615                name="Production Model Evaluation",
2616                description="Monthly evaluation of production model performance",
2617                task=my_production_task,
2618                evaluators=[accuracy_evaluator, latency_evaluator]
2619            )
2620
2621            # Results automatically linked to dataset in Langfuse UI
2622            print(f"View results: {result['dataset_run_url']}")
2623            ```
2624
2625        Note:
2626            - Task and evaluator functions can be either synchronous or asynchronous
2627            - Individual item failures are logged but don't stop the experiment
2628            - All executions are automatically traced and visible in Langfuse UI
2629            - When using Langfuse datasets, results are automatically linked for easy comparison
2630            - This method works in both sync and async contexts (Jupyter notebooks, web apps, etc.)
2631            - Async execution is handled automatically with smart event loop detection
2632        """
2633        return cast(
2634            ExperimentResult,
2635            run_async_safely(
2636                self._run_experiment_async(
2637                    name=name,
2638                    run_name=self._create_experiment_run_name(
2639                        name=name, run_name=run_name
2640                    ),
2641                    description=description,
2642                    data=data,
2643                    task=task,
2644                    evaluators=evaluators or [],
2645                    run_evaluators=run_evaluators or [],
2646                    max_concurrency=max_concurrency,
2647                    metadata=metadata or {},
2648                ),
2649            ),
2650        )
2651
2652    async def _run_experiment_async(
2653        self,
2654        *,
2655        name: str,
2656        run_name: str,
2657        description: Optional[str],
2658        data: ExperimentData,
2659        task: TaskFunction,
2660        evaluators: List[EvaluatorFunction],
2661        run_evaluators: List[RunEvaluatorFunction],
2662        max_concurrency: int,
2663        metadata: Dict[str, Any],
2664    ) -> ExperimentResult:
2665        langfuse_logger.debug(
2666            f"Starting experiment '{name}' run '{run_name}' with {len(data)} items"
2667        )
2668
2669        # Set up concurrency control
2670        semaphore = asyncio.Semaphore(max_concurrency)
2671
2672        # Process all items
2673        async def process_item(item: ExperimentItem) -> ExperimentItemResult:
2674            async with semaphore:
2675                return await self._process_experiment_item(
2676                    item, task, evaluators, name, run_name, description, metadata
2677                )
2678
2679        # Run all items concurrently
2680        tasks = [process_item(item) for item in data]
2681        item_results = await asyncio.gather(*tasks, return_exceptions=True)
2682
2683        # Filter out any exceptions and log errors
2684        valid_results: List[ExperimentItemResult] = []
2685        for i, result in enumerate(item_results):
2686            if isinstance(result, Exception):
2687                langfuse_logger.error(f"Item {i} failed: {result}")
2688            elif isinstance(result, ExperimentItemResult):
2689                valid_results.append(result)  # type: ignore
2690
2691        # Run experiment-level evaluators
2692        run_evaluations: List[Evaluation] = []
2693        for run_evaluator in run_evaluators:
2694            try:
2695                evaluations = await _run_evaluator(
2696                    run_evaluator, item_results=valid_results
2697                )
2698                run_evaluations.extend(evaluations)
2699            except Exception as e:
2700                langfuse_logger.error(f"Run evaluator failed: {e}")
2701
2702        # Generate dataset run URL if applicable
2703        dataset_run_id = valid_results[0].dataset_run_id if valid_results else None
2704        dataset_run_url = None
2705        if dataset_run_id and data:
2706            try:
2707                # Check if the first item has dataset_id (for DatasetItem objects)
2708                first_item = data[0]
2709                dataset_id = None
2710
2711                if hasattr(first_item, "dataset_id"):
2712                    dataset_id = getattr(first_item, "dataset_id", None)
2713
2714                if dataset_id:
2715                    project_id = self._get_project_id()
2716
2717                    if project_id:
2718                        dataset_run_url = f"{self._host}/project/{project_id}/datasets/{dataset_id}/runs/{dataset_run_id}"
2719
2720            except Exception:
2721                pass  # URL generation is optional
2722
2723        # Store run-level evaluations as scores
2724        for evaluation in run_evaluations:
2725            try:
2726                if dataset_run_id:
2727                    self.create_score(
2728                        dataset_run_id=dataset_run_id,
2729                        name=evaluation.name or "<unknown>",
2730                        value=evaluation.value,  # type: ignore
2731                        comment=evaluation.comment,
2732                        metadata=evaluation.metadata,
2733                        data_type=evaluation.data_type,  # type: ignore
2734                    )
2735
2736            except Exception as e:
2737                langfuse_logger.error(f"Failed to store run evaluation: {e}")
2738
2739        # Flush scores and traces
2740        self.flush()
2741
2742        return ExperimentResult(
2743            name=name,
2744            run_name=run_name,
2745            description=description,
2746            item_results=valid_results,
2747            run_evaluations=run_evaluations,
2748            dataset_run_id=dataset_run_id,
2749            dataset_run_url=dataset_run_url,
2750        )
2751
2752    async def _process_experiment_item(
2753        self,
2754        item: ExperimentItem,
2755        task: Callable,
2756        evaluators: List[Callable],
2757        experiment_name: str,
2758        experiment_run_name: str,
2759        experiment_description: Optional[str],
2760        experiment_metadata: Dict[str, Any],
2761    ) -> ExperimentItemResult:
2762        # Execute task with tracing
2763        span_name = "experiment-item-run"
2764
2765        with self.start_as_current_span(name=span_name) as span:
2766            try:
2767                output = await _run_task(task, item)
2768
2769                input_data = (
2770                    item.get("input")
2771                    if isinstance(item, dict)
2772                    else getattr(item, "input", None)
2773                )
2774
2775                item_metadata: Dict[str, Any] = {}
2776
2777                if isinstance(item, dict):
2778                    item_metadata = item.get("metadata", None) or {}
2779
2780                final_metadata = {
2781                    "experiment_name": experiment_name,
2782                    "experiment_run_name": experiment_run_name,
2783                    **experiment_metadata,
2784                }
2785
2786                if (
2787                    not isinstance(item, dict)
2788                    and hasattr(item, "dataset_id")
2789                    and hasattr(item, "id")
2790                ):
2791                    final_metadata.update(
2792                        {"dataset_id": item.dataset_id, "dataset_item_id": item.id}
2793                    )
2794
2795                if isinstance(item_metadata, dict):
2796                    final_metadata.update(item_metadata)
2797
2798                span.update(
2799                    input=input_data,
2800                    output=output,
2801                    metadata=final_metadata,
2802                )
2803
2804                # Get trace ID for linking
2805                trace_id = span.trace_id
2806                dataset_run_id = None
2807
2808                # Link to dataset run if this is a dataset item
2809                if hasattr(item, "id") and hasattr(item, "dataset_id"):
2810                    try:
2811                        dataset_run_item = self.api.dataset_run_items.create(
2812                            request=CreateDatasetRunItemRequest(
2813                                runName=experiment_run_name,
2814                                runDescription=experiment_description,
2815                                metadata=experiment_metadata,
2816                                datasetItemId=item.id,  # type: ignore
2817                                traceId=trace_id,
2818                                observationId=span.id,
2819                            )
2820                        )
2821
2822                        dataset_run_id = dataset_run_item.dataset_run_id
2823
2824                    except Exception as e:
2825                        langfuse_logger.error(f"Failed to create dataset run item: {e}")
2826
2827                # Run evaluators
2828                evaluations = []
2829
2830                for evaluator in evaluators:
2831                    try:
2832                        expected_output = None
2833
2834                        if isinstance(item, dict):
2835                            expected_output = item.get("expected_output")
2836                        elif hasattr(item, "expected_output"):
2837                            expected_output = item.expected_output
2838
2839                        eval_metadata: Optional[Dict[str, Any]] = None
2840
2841                        if isinstance(item, dict):
2842                            eval_metadata = item.get("metadata")
2843                        elif hasattr(item, "metadata"):
2844                            eval_metadata = item.metadata
2845
2846                        eval_results = await _run_evaluator(
2847                            evaluator,
2848                            input=input_data,
2849                            output=output,
2850                            expected_output=expected_output,
2851                            metadata=eval_metadata,
2852                        )
2853                        evaluations.extend(eval_results)
2854
2855                        # Store evaluations as scores
2856                        for evaluation in eval_results:
2857                            self.create_score(
2858                                trace_id=trace_id,
2859                                name=evaluation.name,
2860                                value=evaluation.value or -1,
2861                                comment=evaluation.comment,
2862                                metadata=evaluation.metadata,
2863                            )
2864
2865                    except Exception as e:
2866                        langfuse_logger.error(f"Evaluator failed: {e}")
2867
2868                return ExperimentItemResult(
2869                    item=item,
2870                    output=output,
2871                    evaluations=evaluations,
2872                    trace_id=trace_id,
2873                    dataset_run_id=dataset_run_id,
2874                )
2875
2876            except Exception as e:
2877                span.update(
2878                    output=f"Error: {str(e)}", level="ERROR", status_message=str(e)
2879                )
2880                raise e
2881
2882    def _create_experiment_run_name(
2883        self, *, name: Optional[str] = None, run_name: Optional[str] = None
2884    ) -> str:
2885        if run_name:
2886            return run_name
2887
2888        iso_timestamp = _get_timestamp().isoformat().replace("+00:00", "Z")
2889
2890        return f"{name} - {iso_timestamp}"
2891
2892    def auth_check(self) -> bool:
2893        """Check if the provided credentials (public and secret key) are valid.
2894
2895        Raises:
2896            Exception: If no projects were found for the provided credentials.
2897
2898        Note:
2899            This method is blocking. It is discouraged to use it in production code.
2900        """
2901        try:
2902            projects = self.api.projects.get()
2903            langfuse_logger.debug(
2904                f"Auth check successful, found {len(projects.data)} projects"
2905            )
2906            if len(projects.data) == 0:
2907                raise Exception(
2908                    "Auth check failed, no project found for the keys provided."
2909                )
2910            return True
2911
2912        except AttributeError as e:
2913            langfuse_logger.warning(
2914                f"Auth check failed: Client not properly initialized. Error: {e}"
2915            )
2916            return False
2917
2918        except Error as e:
2919            handle_fern_exception(e)
2920            raise e
2921
2922    def create_dataset(
2923        self,
2924        *,
2925        name: str,
2926        description: Optional[str] = None,
2927        metadata: Optional[Any] = None,
2928    ) -> Dataset:
2929        """Create a dataset with the given name on Langfuse.
2930
2931        Args:
2932            name: Name of the dataset to create.
2933            description: Description of the dataset. Defaults to None.
2934            metadata: Additional metadata. Defaults to None.
2935
2936        Returns:
2937            Dataset: The created dataset as returned by the Langfuse API.
2938        """
2939        try:
2940            body = CreateDatasetRequest(
2941                name=name, description=description, metadata=metadata
2942            )
2943            langfuse_logger.debug(f"Creating datasets {body}")
2944
2945            return self.api.datasets.create(request=body)
2946
2947        except Error as e:
2948            handle_fern_exception(e)
2949            raise e
2950
2951    def create_dataset_item(
2952        self,
2953        *,
2954        dataset_name: str,
2955        input: Optional[Any] = None,
2956        expected_output: Optional[Any] = None,
2957        metadata: Optional[Any] = None,
2958        source_trace_id: Optional[str] = None,
2959        source_observation_id: Optional[str] = None,
2960        status: Optional[DatasetStatus] = None,
2961        id: Optional[str] = None,
2962    ) -> DatasetItem:
2963        """Create a dataset item.
2964
2965        Upserts if an item with id already exists.
2966
2967        Args:
2968            dataset_name: Name of the dataset in which the dataset item should be created.
2969            input: Input data. Defaults to None. Can contain any dict, list or scalar.
2970            expected_output: Expected output data. Defaults to None. Can contain any dict, list or scalar.
2971            metadata: Additional metadata. Defaults to None. Can contain any dict, list or scalar.
2972            source_trace_id: Id of the source trace. Defaults to None.
2973            source_observation_id: Id of the source observation. Defaults to None.
2974            status: Status of the dataset item. Defaults to ACTIVE for newly created items.
2975            id: Id of the dataset item. Defaults to None. Provide your own id if you want to dedupe dataset items. Id needs to be globally unique and cannot be reused across datasets.
2976
2977        Returns:
2978            DatasetItem: The created dataset item as returned by the Langfuse API.
2979
2980        Example:
2981            ```python
2982            from langfuse import Langfuse
2983
2984            langfuse = Langfuse()
2985
2986            # Uploading items to the Langfuse dataset named "capital_cities"
2987            langfuse.create_dataset_item(
2988                dataset_name="capital_cities",
2989                input={"input": {"country": "Italy"}},
2990                expected_output={"expected_output": "Rome"},
2991                metadata={"foo": "bar"}
2992            )
2993            ```
2994        """
2995        try:
2996            body = CreateDatasetItemRequest(
2997                datasetName=dataset_name,
2998                input=input,
2999                expectedOutput=expected_output,
3000                metadata=metadata,
3001                sourceTraceId=source_trace_id,
3002                sourceObservationId=source_observation_id,
3003                status=status,
3004                id=id,
3005            )
3006            langfuse_logger.debug(f"Creating dataset item {body}")
3007            return self.api.dataset_items.create(request=body)
3008        except Error as e:
3009            handle_fern_exception(e)
3010            raise e
3011
3012    def resolve_media_references(
3013        self,
3014        *,
3015        obj: Any,
3016        resolve_with: Literal["base64_data_uri"],
3017        max_depth: int = 10,
3018        content_fetch_timeout_seconds: int = 5,
3019    ) -> Any:
3020        """Replace media reference strings in an object with base64 data URIs.
3021
3022        This method recursively traverses an object (up to max_depth) looking for media reference strings
3023        in the format "@@@langfuseMedia:...@@@". When found, it (synchronously) fetches the actual media content using
3024        the provided Langfuse client and replaces the reference string with a base64 data URI.
3025
3026        If fetching media content fails for a reference string, a warning is logged and the reference
3027        string is left unchanged.
3028
3029        Args:
3030            obj: The object to process. Can be a primitive value, array, or nested object.
3031                If the object has a __dict__ attribute, a dict will be returned instead of the original object type.
3032            resolve_with: The representation of the media content to replace the media reference string with.
3033                Currently only "base64_data_uri" is supported.
3034            max_depth: int: The maximum depth to traverse the object. Default is 10.
3035            content_fetch_timeout_seconds: int: The timeout in seconds for fetching media content. Default is 5.
3036
3037        Returns:
3038            A deep copy of the input object with all media references replaced with base64 data URIs where possible.
3039            If the input object has a __dict__ attribute, a dict will be returned instead of the original object type.
3040
3041        Example:
3042            obj = {
3043                "image": "@@@langfuseMedia:type=image/jpeg|id=123|source=bytes@@@",
3044                "nested": {
3045                    "pdf": "@@@langfuseMedia:type=application/pdf|id=456|source=bytes@@@"
3046                }
3047            }
3048
3049            result = await LangfuseMedia.resolve_media_references(obj, langfuse_client)
3050
3051            # Result:
3052            # {
3053            #     "image": "data:image/jpeg;base64,/9j/4AAQSkZJRg...",
3054            #     "nested": {
3055            #         "pdf": "data:application/pdf;base64,JVBERi0xLjcK..."
3056            #     }
3057            # }
3058        """
3059        return LangfuseMedia.resolve_media_references(
3060            langfuse_client=self,
3061            obj=obj,
3062            resolve_with=resolve_with,
3063            max_depth=max_depth,
3064            content_fetch_timeout_seconds=content_fetch_timeout_seconds,
3065        )
3066
3067    @overload
3068    def get_prompt(
3069        self,
3070        name: str,
3071        *,
3072        version: Optional[int] = None,
3073        label: Optional[str] = None,
3074        type: Literal["chat"],
3075        cache_ttl_seconds: Optional[int] = None,
3076        fallback: Optional[List[ChatMessageDict]] = None,
3077        max_retries: Optional[int] = None,
3078        fetch_timeout_seconds: Optional[int] = None,
3079    ) -> ChatPromptClient: ...
3080
3081    @overload
3082    def get_prompt(
3083        self,
3084        name: str,
3085        *,
3086        version: Optional[int] = None,
3087        label: Optional[str] = None,
3088        type: Literal["text"] = "text",
3089        cache_ttl_seconds: Optional[int] = None,
3090        fallback: Optional[str] = None,
3091        max_retries: Optional[int] = None,
3092        fetch_timeout_seconds: Optional[int] = None,
3093    ) -> TextPromptClient: ...
3094
3095    def get_prompt(
3096        self,
3097        name: str,
3098        *,
3099        version: Optional[int] = None,
3100        label: Optional[str] = None,
3101        type: Literal["chat", "text"] = "text",
3102        cache_ttl_seconds: Optional[int] = None,
3103        fallback: Union[Optional[List[ChatMessageDict]], Optional[str]] = None,
3104        max_retries: Optional[int] = None,
3105        fetch_timeout_seconds: Optional[int] = None,
3106    ) -> PromptClient:
3107        """Get a prompt.
3108
3109        This method attempts to fetch the requested prompt from the local cache. If the prompt is not found
3110        in the cache or if the cached prompt has expired, it will try to fetch the prompt from the server again
3111        and update the cache. If fetching the new prompt fails, and there is an expired prompt in the cache, it will
3112        return the expired prompt as a fallback.
3113
3114        Args:
3115            name (str): The name of the prompt to retrieve.
3116
3117        Keyword Args:
3118            version (Optional[int]): The version of the prompt to retrieve. If no label and version is specified, the `production` label is returned. Specify either version or label, not both.
3119            label: Optional[str]: The label of the prompt to retrieve. If no label and version is specified, the `production` label is returned. Specify either version or label, not both.
3120            cache_ttl_seconds: Optional[int]: Time-to-live in seconds for caching the prompt. Must be specified as a
3121            keyword argument. If not set, defaults to 60 seconds. Disables caching if set to 0.
3122            type: Literal["chat", "text"]: The type of the prompt to retrieve. Defaults to "text".
3123            fallback: Union[Optional[List[ChatMessageDict]], Optional[str]]: The prompt string to return if fetching the prompt fails. Important on the first call where no cached prompt is available. Follows Langfuse prompt formatting with double curly braces for variables. Defaults to None.
3124            max_retries: Optional[int]: The maximum number of retries in case of API/network errors. Defaults to 2. The maximum value is 4. Retries have an exponential backoff with a maximum delay of 10 seconds.
3125            fetch_timeout_seconds: Optional[int]: The timeout in milliseconds for fetching the prompt. Defaults to the default timeout set on the SDK, which is 5 seconds per default.
3126
3127        Returns:
3128            The prompt object retrieved from the cache or directly fetched if not cached or expired of type
3129            - TextPromptClient, if type argument is 'text'.
3130            - ChatPromptClient, if type argument is 'chat'.
3131
3132        Raises:
3133            Exception: Propagates any exceptions raised during the fetching of a new prompt, unless there is an
3134            expired prompt in the cache, in which case it logs a warning and returns the expired prompt.
3135        """
3136        if self._resources is None:
3137            raise Error(
3138                "SDK is not correctly initialized. Check the init logs for more details."
3139            )
3140        if version is not None and label is not None:
3141            raise ValueError("Cannot specify both version and label at the same time.")
3142
3143        if not name:
3144            raise ValueError("Prompt name cannot be empty.")
3145
3146        cache_key = PromptCache.generate_cache_key(name, version=version, label=label)
3147        bounded_max_retries = self._get_bounded_max_retries(
3148            max_retries, default_max_retries=2, max_retries_upper_bound=4
3149        )
3150
3151        langfuse_logger.debug(f"Getting prompt '{cache_key}'")
3152        cached_prompt = self._resources.prompt_cache.get(cache_key)
3153
3154        if cached_prompt is None or cache_ttl_seconds == 0:
3155            langfuse_logger.debug(
3156                f"Prompt '{cache_key}' not found in cache or caching disabled."
3157            )
3158            try:
3159                return self._fetch_prompt_and_update_cache(
3160                    name,
3161                    version=version,
3162                    label=label,
3163                    ttl_seconds=cache_ttl_seconds,
3164                    max_retries=bounded_max_retries,
3165                    fetch_timeout_seconds=fetch_timeout_seconds,
3166                )
3167            except Exception as e:
3168                if fallback:
3169                    langfuse_logger.warning(
3170                        f"Returning fallback prompt for '{cache_key}' due to fetch error: {e}"
3171                    )
3172
3173                    fallback_client_args: Dict[str, Any] = {
3174                        "name": name,
3175                        "prompt": fallback,
3176                        "type": type,
3177                        "version": version or 0,
3178                        "config": {},
3179                        "labels": [label] if label else [],
3180                        "tags": [],
3181                    }
3182
3183                    if type == "text":
3184                        return TextPromptClient(
3185                            prompt=Prompt_Text(**fallback_client_args),
3186                            is_fallback=True,
3187                        )
3188
3189                    if type == "chat":
3190                        return ChatPromptClient(
3191                            prompt=Prompt_Chat(**fallback_client_args),
3192                            is_fallback=True,
3193                        )
3194
3195                raise e
3196
3197        if cached_prompt.is_expired():
3198            langfuse_logger.debug(f"Stale prompt '{cache_key}' found in cache.")
3199            try:
3200                # refresh prompt in background thread, refresh_prompt deduplicates tasks
3201                langfuse_logger.debug(f"Refreshing prompt '{cache_key}' in background.")
3202
3203                def refresh_task() -> None:
3204                    self._fetch_prompt_and_update_cache(
3205                        name,
3206                        version=version,
3207                        label=label,
3208                        ttl_seconds=cache_ttl_seconds,
3209                        max_retries=bounded_max_retries,
3210                        fetch_timeout_seconds=fetch_timeout_seconds,
3211                    )
3212
3213                self._resources.prompt_cache.add_refresh_prompt_task(
3214                    cache_key,
3215                    refresh_task,
3216                )
3217                langfuse_logger.debug(
3218                    f"Returning stale prompt '{cache_key}' from cache."
3219                )
3220                # return stale prompt
3221                return cached_prompt.value
3222
3223            except Exception as e:
3224                langfuse_logger.warning(
3225                    f"Error when refreshing cached prompt '{cache_key}', returning cached version. Error: {e}"
3226                )
3227                # creation of refresh prompt task failed, return stale prompt
3228                return cached_prompt.value
3229
3230        return cached_prompt.value
3231
3232    def _fetch_prompt_and_update_cache(
3233        self,
3234        name: str,
3235        *,
3236        version: Optional[int] = None,
3237        label: Optional[str] = None,
3238        ttl_seconds: Optional[int] = None,
3239        max_retries: int,
3240        fetch_timeout_seconds: Optional[int],
3241    ) -> PromptClient:
3242        cache_key = PromptCache.generate_cache_key(name, version=version, label=label)
3243        langfuse_logger.debug(f"Fetching prompt '{cache_key}' from server...")
3244
3245        try:
3246
3247            @backoff.on_exception(
3248                backoff.constant, Exception, max_tries=max_retries + 1, logger=None
3249            )
3250            def fetch_prompts() -> Any:
3251                return self.api.prompts.get(
3252                    self._url_encode(name),
3253                    version=version,
3254                    label=label,
3255                    request_options={
3256                        "timeout_in_seconds": fetch_timeout_seconds,
3257                    }
3258                    if fetch_timeout_seconds is not None
3259                    else None,
3260                )
3261
3262            prompt_response = fetch_prompts()
3263
3264            prompt: PromptClient
3265            if prompt_response.type == "chat":
3266                prompt = ChatPromptClient(prompt_response)
3267            else:
3268                prompt = TextPromptClient(prompt_response)
3269
3270            if self._resources is not None:
3271                self._resources.prompt_cache.set(cache_key, prompt, ttl_seconds)
3272
3273            return prompt
3274
3275        except Exception as e:
3276            langfuse_logger.error(
3277                f"Error while fetching prompt '{cache_key}': {str(e)}"
3278            )
3279            raise e
3280
3281    def _get_bounded_max_retries(
3282        self,
3283        max_retries: Optional[int],
3284        *,
3285        default_max_retries: int = 2,
3286        max_retries_upper_bound: int = 4,
3287    ) -> int:
3288        if max_retries is None:
3289            return default_max_retries
3290
3291        bounded_max_retries = min(
3292            max(max_retries, 0),
3293            max_retries_upper_bound,
3294        )
3295
3296        return bounded_max_retries
3297
3298    @overload
3299    def create_prompt(
3300        self,
3301        *,
3302        name: str,
3303        prompt: List[Union[ChatMessageDict, ChatMessageWithPlaceholdersDict]],
3304        labels: List[str] = [],
3305        tags: Optional[List[str]] = None,
3306        type: Optional[Literal["chat"]],
3307        config: Optional[Any] = None,
3308        commit_message: Optional[str] = None,
3309    ) -> ChatPromptClient: ...
3310
3311    @overload
3312    def create_prompt(
3313        self,
3314        *,
3315        name: str,
3316        prompt: str,
3317        labels: List[str] = [],
3318        tags: Optional[List[str]] = None,
3319        type: Optional[Literal["text"]] = "text",
3320        config: Optional[Any] = None,
3321        commit_message: Optional[str] = None,
3322    ) -> TextPromptClient: ...
3323
3324    def create_prompt(
3325        self,
3326        *,
3327        name: str,
3328        prompt: Union[
3329            str, List[Union[ChatMessageDict, ChatMessageWithPlaceholdersDict]]
3330        ],
3331        labels: List[str] = [],
3332        tags: Optional[List[str]] = None,
3333        type: Optional[Literal["chat", "text"]] = "text",
3334        config: Optional[Any] = None,
3335        commit_message: Optional[str] = None,
3336    ) -> PromptClient:
3337        """Create a new prompt in Langfuse.
3338
3339        Keyword Args:
3340            name : The name of the prompt to be created.
3341            prompt : The content of the prompt to be created.
3342            is_active [DEPRECATED] : A flag indicating whether the prompt is active or not. This is deprecated and will be removed in a future release. Please use the 'production' label instead.
3343            labels: The labels of the prompt. Defaults to None. To create a default-served prompt, add the 'production' label.
3344            tags: The tags of the prompt. Defaults to None. Will be applied to all versions of the prompt.
3345            config: Additional structured data to be saved with the prompt. Defaults to None.
3346            type: The type of the prompt to be created. "chat" vs. "text". Defaults to "text".
3347            commit_message: Optional string describing the change.
3348
3349        Returns:
3350            TextPromptClient: The prompt if type argument is 'text'.
3351            ChatPromptClient: The prompt if type argument is 'chat'.
3352        """
3353        try:
3354            langfuse_logger.debug(f"Creating prompt {name=}, {labels=}")
3355
3356            if type == "chat":
3357                if not isinstance(prompt, list):
3358                    raise ValueError(
3359                        "For 'chat' type, 'prompt' must be a list of chat messages with role and content attributes."
3360                    )
3361                request: Union[CreatePromptRequest_Chat, CreatePromptRequest_Text] = (
3362                    CreatePromptRequest_Chat(
3363                        name=name,
3364                        prompt=cast(Any, prompt),
3365                        labels=labels,
3366                        tags=tags,
3367                        config=config or {},
3368                        commitMessage=commit_message,
3369                        type="chat",
3370                    )
3371                )
3372                server_prompt = self.api.prompts.create(request=request)
3373
3374                if self._resources is not None:
3375                    self._resources.prompt_cache.invalidate(name)
3376
3377                return ChatPromptClient(prompt=cast(Prompt_Chat, server_prompt))
3378
3379            if not isinstance(prompt, str):
3380                raise ValueError("For 'text' type, 'prompt' must be a string.")
3381
3382            request = CreatePromptRequest_Text(
3383                name=name,
3384                prompt=prompt,
3385                labels=labels,
3386                tags=tags,
3387                config=config or {},
3388                commitMessage=commit_message,
3389                type="text",
3390            )
3391
3392            server_prompt = self.api.prompts.create(request=request)
3393
3394            if self._resources is not None:
3395                self._resources.prompt_cache.invalidate(name)
3396
3397            return TextPromptClient(prompt=cast(Prompt_Text, server_prompt))
3398
3399        except Error as e:
3400            handle_fern_exception(e)
3401            raise e
3402
3403    def update_prompt(
3404        self,
3405        *,
3406        name: str,
3407        version: int,
3408        new_labels: List[str] = [],
3409    ) -> Any:
3410        """Update an existing prompt version in Langfuse. The Langfuse SDK prompt cache is invalidated for all prompts witht he specified name.
3411
3412        Args:
3413            name (str): The name of the prompt to update.
3414            version (int): The version number of the prompt to update.
3415            new_labels (List[str], optional): New labels to assign to the prompt version. Labels are unique across versions. The "latest" label is reserved and managed by Langfuse. Defaults to [].
3416
3417        Returns:
3418            Prompt: The updated prompt from the Langfuse API.
3419
3420        """
3421        updated_prompt = self.api.prompt_version.update(
3422            name=self._url_encode(name),
3423            version=version,
3424            new_labels=new_labels,
3425        )
3426
3427        if self._resources is not None:
3428            self._resources.prompt_cache.invalidate(name)
3429
3430        return updated_prompt
3431
3432    def _url_encode(self, url: str, *, is_url_param: Optional[bool] = False) -> str:
3433        # httpx ≥ 0.28 does its own WHATWG-compliant quoting (eg. encodes bare
3434        # “%”, “?”, “#”, “|”, … in query/path parts).  Re-quoting here would
3435        # double-encode, so we skip when the value is about to be sent straight
3436        # to httpx (`is_url_param=True`) and the installed version is ≥ 0.28.
3437        if is_url_param and Version(httpx.__version__) >= Version("0.28.0"):
3438            return url
3439
3440        # urllib.parse.quote does not escape slashes "/" by default; we need to add safe="" to force escaping
3441        # we need add safe="" to force escaping of slashes
3442        # This is necessary for prompts in prompt folders
3443        return urllib.parse.quote(url, safe="")
3444
3445    def clear_prompt_cache(self) -> None:
3446        """Clear the entire prompt cache, removing all cached prompts.
3447
3448        This method is useful when you want to force a complete refresh of all
3449        cached prompts, for example after major updates or when you need to
3450        ensure the latest versions are fetched from the server.
3451        """
3452        if self._resources is not None:
3453            self._resources.prompt_cache.clear()