diff --git a/src/strands_evals/mappers/constants.py b/src/strands_evals/mappers/constants.py index 884f80da..4f201eb1 100644 --- a/src/strands_evals/mappers/constants.py +++ b/src/strands_evals/mappers/constants.py @@ -7,8 +7,17 @@ # --- Instrumentation scope names --- SCOPE_LANGCHAIN_OTEL = "opentelemetry.instrumentation.langchain" SCOPE_OPENINFERENCE = "openinference.instrumentation.langchain" +SCOPE_OPENINFERENCE_SMOLAGENTS = "openinference.instrumentation.smolagents" SCOPE_STRANDS = "strands.telemetry.tracer" +# All scopes that should route to OpenInferenceSessionMapper +SCOPES_OPENINFERENCE_FAMILY = frozenset( + [ + SCOPE_OPENINFERENCE, + SCOPE_OPENINFERENCE_SMOLAGENTS, + ] +) + # --- OTEL semantic convention attribute keys --- ATTR_LLM_REQUEST_TYPE = "llm.request.type" diff --git a/src/strands_evals/mappers/openinference_session_mapper.py b/src/strands_evals/mappers/openinference_session_mapper.py index 6ecfee4d..47286c3f 100644 --- a/src/strands_evals/mappers/openinference_session_mapper.py +++ b/src/strands_evals/mappers/openinference_session_mapper.py @@ -1,7 +1,15 @@ """ -OpenInferenceSessionMapper - Maps OpenInference LangChain traces to Session format. +OpenInferenceSessionMapper - Maps OpenInference traces to Session format. -Handles traces with scope: openinference.instrumentation.langchain +Handles traces from any producer in the OpenInference family: +- openinference.instrumentation.langchain (LangChain / LangGraph) +- openinference.instrumentation.smolagents (HuggingFace smolagents) + +Each producer emits spans following the OpenInference semantic conventions but +with producer-specific encoding differences (e.g. attribute paths for message +content, tool argument wrapping). A per-producer normalization step inside +map_to_session() canonicalizes these differences before the shared conversion +logic runs. """ import ast @@ -27,20 +35,27 @@ Trace, UserMessage, ) -from .constants import SCOPE_OPENINFERENCE +from .constants import SCOPE_OPENINFERENCE_SMOLAGENTS, SCOPES_OPENINFERENCE_FAMILY from .session_mapper import SessionMapper logger = logging.getLogger(__name__) class OpenInferenceSessionMapper(SessionMapper): - """Maps OpenInference LangChain traces to Session format. + """Maps OpenInference traces to Session format. + + This mapper handles traces produced by any library in the OpenInference family: + - openinference-instrumentation-langchain (LangChain / LangGraph) + - openinference-instrumentation-smolagents (HuggingFace smolagents) - This mapper handles traces produced by the openinference-instrumentation-langchain library. - It identifies span types using: - - Inference spans: openinference.span.kind == "LLM" - - Tool execution spans: openinference.span.kind == "TOOL" - - Agent invocation spans: openinference.span.kind == "CHAIN" (name=LangGraph) or "AGENT" + Span type identification uses the openinference.span.kind attribute: + - Inference spans: "LLM" + - Tool execution spans: "TOOL" + - Agent invocation spans: "AGENT" (smolagents CodeAgent.run) or + "CHAIN" with name="LangGraph" (LangGraph root graph) + + Producer-specific encoding differences (e.g. message attribute paths, + tool argument wrapping) are normalized before shared conversion logic runs. """ def __init__(self): @@ -56,7 +71,7 @@ def __init__(self): self._adot_output_cache: dict[str, Any] = {} def map_to_session(self, data: Any, session_id: str) -> Session: - """Map OpenInference LangChain spans to Session format. + """Map OpenInference spans to Session format. Args: data: Trace data in various formats: @@ -77,8 +92,14 @@ def map_to_session(self, data: Any, session_id: str) -> Session: # Normalize input to flat spans spans = self._normalize_to_flat_spans(data) - # Filter to only spans from this scope - openinference_spans = [s for s in spans if self._get_scope_name(s) == SCOPE_OPENINFERENCE] + # Filter to only spans from this scope (including smolagents variant) + openinference_spans = [s for s in spans if self._get_scope_name(s) in SCOPES_OPENINFERENCE_FAMILY] + + # Per-producer normalization: canonicalize encoding differences so the + # shared conversion logic receives a uniform representation. + for span in openinference_spans: + if self._get_scope_name(span) == SCOPE_OPENINFERENCE_SMOLAGENTS: + self._normalize_smolagents_span(span) # Group spans by trace_id grouped = defaultdict(list) @@ -95,6 +116,122 @@ def map_to_session(self, data: Any, session_id: str) -> Session: return Session(traces=result_traces, session_id=session_id) + # ========================================================================= + # Per-producer normalization + # ========================================================================= + + def _normalize_smolagents_span(self, span: dict) -> None: + """Normalize a smolagents span in-place to the canonical representation. + + Smolagents OpenInference instrumentation differs from the LangChain variant: + 1. LLM message content uses plural path: + llm.input_messages.N.message.contents.0.message_content.text + instead of singular: llm.input_messages.N.message.content + 2. Tool input.value wraps arguments in: + {"args": [...], "kwargs": {...}, "sanitize_inputs_outputs": ...} + instead of clean logical arguments. + 3. AGENT spans carry user_task (input.value) and final response (output.value) + as plain strings rather than structured messages. + """ + attrs = span.get("attributes", {}) + span_kind = attrs.get("openinference.span.kind", "") + + if span_kind == "LLM": + self._normalize_smolagents_llm_attrs(attrs) + elif span_kind == "TOOL": + self._normalize_smolagents_tool_attrs(attrs) + + def _normalize_smolagents_llm_attrs(self, attrs: dict) -> None: + """Normalize smolagents LLM span attributes to canonical form. + + Converts plural contents path to singular content path: + llm.input_messages.N.message.contents.0.message_content.text + → llm.input_messages.N.message.content + + llm.output_messages.N.message.contents.0.message_content.text + → llm.output_messages.N.message.content + """ + # Normalize input messages + idx = 0 + while True: + prefix = f"llm.input_messages.{idx}.message" + role = attrs.get(f"{prefix}.role") + if role is None: + break + # If singular .content is missing, try plural .contents path + if not attrs.get(f"{prefix}.content"): + content_text = attrs.get(f"{prefix}.contents.0.message_content.text") + if content_text: + attrs[f"{prefix}.content"] = content_text + idx += 1 + + # Normalize output messages + idx = 0 + while True: + prefix = f"llm.output_messages.{idx}.message" + role = attrs.get(f"{prefix}.role") + if role is None: + break + if not attrs.get(f"{prefix}.content"): + content_text = attrs.get(f"{prefix}.contents.0.message_content.text") + if content_text: + attrs[f"{prefix}.content"] = content_text + idx += 1 + + def _normalize_smolagents_tool_attrs(self, attrs: dict) -> None: + """Normalize smolagents TOOL span input.value to logical arguments. + + Smolagents instrumentation wraps tool calls as: + {"args": [positional_args...], "kwargs": {named_args...}, + "sanitize_inputs_outputs": bool} + + This normalizes to the logical arguments by: + 1. Using kwargs directly when present (most common case) + 2. Mapping positional args to named parameters using tool.parameters + 3. Merging both when a tool uses positional + keyword args + """ + input_value = attrs.get("input.value") + if not input_value or not isinstance(input_value, str): + return + + try: + parsed = json.loads(input_value) + except json.JSONDecodeError: + return + + if not isinstance(parsed, dict): + return + + # Only normalize if it looks like the smolagents wrapper format + if "kwargs" not in parsed and "args" not in parsed: + return + + kwargs = parsed.get("kwargs", {}) + args = parsed.get("args", []) + + # Map positional args to named parameters using tool.parameters + named_from_args: dict = {} + if args and isinstance(args, list): + tool_params_raw = attrs.get("tool.parameters") + if tool_params_raw: + try: + tool_params = json.loads(tool_params_raw) if isinstance(tool_params_raw, str) else tool_params_raw + if isinstance(tool_params, dict): + param_names = list(tool_params.keys()) + for i, arg_val in enumerate(args): + if i < len(param_names): + named_from_args[param_names[i]] = arg_val + except (json.JSONDecodeError, TypeError): + pass + + # Build the logical arguments: named positional args + kwargs merged + if named_from_args or (kwargs and isinstance(kwargs, dict)): + logical_args = {**named_from_args, **(kwargs if isinstance(kwargs, dict) else {})} + attrs["input.value"] = json.dumps(logical_args) + elif args and isinstance(args, list): + # Fallback: no tool.parameters available to map positional args + attrs["input.value"] = json.dumps({"args": args}) + def _build_trace(self, trace_id: str, spans: list[dict], session_id: str) -> Trace: """Build a Trace from spans with the same trace_id.""" converted_spans: list[InferenceSpan | ToolExecutionSpan | AgentInvocationSpan] = [] @@ -189,17 +326,35 @@ def _is_agent_invocation_span(self, span: dict) -> bool: """Check if span is an agent invocation span. Detection: - 1. Live instrumentation: CHAIN + name=LangGraph - 2. ADOT body: root LangGraph graph node — input has "messages" without + 1. Live instrumentation (LangGraph): CHAIN + name=LangGraph + 2. Live instrumentation (smolagents): AGENT span kind + 3. ADOT body: root LangGraph graph node — input has "messages" without "remaining_steps" (intermediate nodes always have "remaining_steps"), and output has "messages". """ attrs = span.get("attributes", {}) span_kind = attrs.get("openinference.span.kind", "") span_name = span.get("name", "") + + # LangGraph: CHAIN span named "LangGraph" if span_kind == "CHAIN" and span_name == "LangGraph": return True + # smolagents: AGENT span (e.g. CodeAgent.run) — only accept spans from + # the smolagents scope to avoid matching LangChain's route_to_agent spans + # which also have kind=AGENT with both input and output values. + if span_kind == "AGENT": + scope_name = self._get_scope_name(span) + if scope_name == SCOPE_OPENINFERENCE_SMOLAGENTS: + input_val = attrs.get("input.value") + output_val = attrs.get("output.value") + if input_val and output_val: + return True + # LangChain AGENT spans (e.g. route_to_agent) carry kind=AGENT with + # input/output but are routing nodes, not agent invocations. Explicitly + # reject any non-smolagents AGENT span. + return False + # ADOT fallback: root LangGraph node has messages in/out but no remaining_steps. # Intermediate agent nodes (from create_react_agent) always include # "remaining_steps" in input — the root graph invocation does not. @@ -273,7 +428,7 @@ def _convert_tool_execution_span(self, span: dict, session_id: str) -> ToolExecu if not tool_name: span_name = span.get("name", "") # ADOT synthetic spans use the scope name as span name — skip it - if span_name and span_name != SCOPE_OPENINFERENCE: + if span_name and span_name not in SCOPES_OPENINFERENCE_FAMILY: tool_name = span_name # Get input from attributes @@ -294,16 +449,19 @@ def _convert_tool_execution_span(self, span: dict, session_id: str) -> ToolExecu # Get output from attributes output_value = attrs.get("output.value") if output_value: - try: - if isinstance(output_value, str): + if isinstance(output_value, str): + try: parsed = json.loads(output_value) + except json.JSONDecodeError: + parsed = None + if isinstance(parsed, dict): tool_output_content = parsed.get("content", str(parsed)) tool_call_id = parsed.get("tool_call_id") tool_status = parsed.get("status", "success") - elif isinstance(output_value, dict): - tool_output_content = output_value.get("content", str(output_value)) - except json.JSONDecodeError: - tool_output_content = str(output_value) + else: + tool_output_content = output_value + elif isinstance(output_value, dict): + tool_output_content = output_value.get("content", str(output_value)) # Fallback to span_events format (CloudWatch/ADOT) if not tool_name or tool_parameters is None or tool_output_content is None: @@ -356,14 +514,45 @@ def _convert_tool_execution_span(self, span: dict, session_id: str) -> ToolExecu return ToolExecutionSpan(span_info=span_info, tool_call=tool_call, tool_result=tool_result, metadata={}) def _convert_agent_invocation_span(self, span: dict, session_id: str) -> AgentInvocationSpan | None: - """Convert OTEL span to AgentInvocationSpan.""" + """Convert OTEL span to AgentInvocationSpan. + + Handles two formats: + - LangGraph: structured messages in span_events body + - smolagents: input.value (user task) and output.value (final answer) as plain strings + """ span_info = self._create_span_info(span, session_id) trace_id = span.get("trace_id", "") + attrs = span.get("attributes", {}) - input_messages, output_messages = self._get_messages_from_span_events(span) + user_prompt: str | None = None + agent_response: str | None = None - user_prompt = self._extract_user_prompt(input_messages, span) - agent_response = self._extract_agent_response(output_messages, span) + # smolagents AGENT spans: input.value is a JSON object with "task" field, + # output.value is the final response string + span_kind = attrs.get("openinference.span.kind", "") + if span_kind == "AGENT": + input_value = attrs.get("input.value", "") + output_value = attrs.get("output.value", "") + if isinstance(input_value, str) and input_value: + # smolagents wraps user task in: {"task": "...", "stream": ..., ...} + try: + parsed_input = json.loads(input_value) + if isinstance(parsed_input, dict) and "task" in parsed_input: + user_prompt = parsed_input["task"] + else: + user_prompt = input_value + except (json.JSONDecodeError, TypeError): + user_prompt = input_value + if isinstance(output_value, str) and output_value: + agent_response = output_value + + # LangGraph / ADOT: extract from structured messages + if not user_prompt or not agent_response: + input_messages, output_messages = self._get_messages_from_span_events(span) + if not user_prompt: + user_prompt = self._extract_user_prompt(input_messages, span) + if not agent_response: + agent_response = self._extract_agent_response(output_messages, span) if not user_prompt: logger.warning(f"No user_prompt for agent span {span.get('span_id')}") @@ -495,7 +684,7 @@ def _extract_messages_from_span(self, span: dict) -> tuple[list[dict], list[dict span_events = span.get("span_events", []) for event in span_events: event_name = event.get("event_name", "") - if event_name == SCOPE_OPENINFERENCE: + if event_name in SCOPES_OPENINFERENCE_FAMILY: body = event.get("body", {}) input_group = body.get("input", {}) output_group = body.get("output", {}) diff --git a/src/strands_evals/mappers/utils.py b/src/strands_evals/mappers/utils.py index 82058385..1944bf0b 100644 --- a/src/strands_evals/mappers/utils.py +++ b/src/strands_evals/mappers/utils.py @@ -6,7 +6,7 @@ import logging from typing import Any -from .constants import SCOPE_LANGCHAIN_OTEL, SCOPE_OPENINFERENCE, SCOPE_STRANDS +from .constants import SCOPE_LANGCHAIN_OTEL, SCOPE_STRANDS, SCOPES_OPENINFERENCE_FAMILY from .session_mapper import SessionMapper logger = logging.getLogger(__name__) @@ -104,7 +104,7 @@ def detect_otel_mapper(spans: list[Any]) -> SessionMapper: if scope_name == SCOPE_LANGCHAIN_OTEL: return LangChainOtelSessionMapper() - if scope_name == SCOPE_OPENINFERENCE: + if scope_name in SCOPES_OPENINFERENCE_FAMILY: return OpenInferenceSessionMapper() if scope_name == SCOPE_STRANDS: diff --git a/tests/strands_evals/mappers/fixtures/smolagents_live_spans.json b/tests/strands_evals/mappers/fixtures/smolagents_live_spans.json new file mode 100644 index 00000000..f83f70e5 --- /dev/null +++ b/tests/strands_evals/mappers/fixtures/smolagents_live_spans.json @@ -0,0 +1,206 @@ +[ + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "527aa0aa58906c7f", + "parent_span_id": "11314f4c80c23993", + "name": "LiteLLMModel.generate", + "start_time": 1784230697964446628, + "end_time": 1784230699179110010, + "attributes": { + "input.mime_type": "application/json", + "input.value": "{\"messages\": [\"ChatMessage(role=, content=[{'type': 'text', 'text': 'You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\\\\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\\\\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.\\\\n\\\\nAt each step, in the \\\\'Thought:\\\\' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\\\\nThen in the Code sequence you should write the code in simple Python. The code sequence must be opened with \\\\'\\\\', and closed with \\\\'\\\\'.\\\\nDuring each intermediate step, you can use \\\\'print()\\\\' to save whatever important information you will then need.\\\\nThese print outputs will then appear in the \\\\'Observation:\\\\' field, which will be available as input for the next step.\\\\nIn the end you have to return a final answer using the `final_answer` tool.\\\\n\\\\nHere are a few examples using notional tools:\\\\n---\\\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\\\n\\\\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\\\\n\\\\nanswer = document_qa(document=document, question=\\\"Who is the oldest person mentioned?\\\")\\\\nprint(answer)\\\\n\\\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\\\n\\\\nThought: I will now generate an image showcasing the oldest person.\\\\n\\\\nimage = image_generator(\\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\")\\\\nfinal_answer(image)\\\\n\\\\n\\\\n---\\\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\\\n\\\\nThought: I will use Python code to compute the result of the operation and then return the final answer using the `final_answer` tool.\\\\n\\\\nresult = 5 + 3 + 1294.678\\\\nfinal_answer(result)\\\\n\\\\n\\\\n---\\\\nTask:\\\\n\\\"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\\\\nYou have been provided with these additional arguments, that you can access using the keys as variables in your Python code:\\\\n{\\\\'question\\\\': \\\\'Quel est l\\\\'animal sur l\\\\'image?\\\\', \\\\'image\\\\': \\\\'path/to/image.jpg\\\\'}\\\"\\\\n\\\\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\\\\n\\\\ntranslated_question = translator(question=question, src_lang=\\\"French\\\", tgt_lang=\\\"English\\\")\\\\nprint(f\\\"The translated question is {translated_question}.\\\")\\\\nanswer = image_qa(image=image, question=translated_question)\\\\nfinal_answer(f\\\"The answer is {answer}\\\")\\\\n\\\\n\\\\n---\\\\nTask:\\\\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\\\\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\\\\n\\\\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\\\\n\\\\npages = web_search(query=\\\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\\\")\\\\nprint(pages)\\\\n\\\\nObservation:\\\\nNo result found for query \\\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\\\".\\\\n\\\\nThought: The query was maybe too restrictive and did not find any results. Let\\\\'s try again with a broader query.\\\\n\\\\npages = web_search(query=\\\"1979 interview Stanislaus Ulam\\\")\\\\nprint(pages)\\\\n\\\\nObservation:\\\\nFound 6 pages:\\\\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\\\\n\\\\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\\\\n\\\\n(truncated)\\\\n\\\\nThought: I will read the first 2 pages to know more.\\\\n\\\\nfor url in [\\\"https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/\\\", \\\"https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/\\\"]:\\\\n whole_page = visit_webpage(url)\\\\n print(whole_page)\\\\n print(\\\"\\\\\\\\n\\\" + \\\"=\\\"*80 + \\\"\\\\\\\\n\\\") # Print separator between pages\\\\n\\\\nObservation:\\\\nManhattan Project Locations:\\\\nLos Alamos, NM\\\\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\\\\n(truncated)\\\\n\\\\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: \\\"He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity.\\\" Let\\\\'s answer in one word.\\\\n\\\\nfinal_answer(\\\"diminished\\\")\\\\n\\\\n\\\\n---\\\\nTask: \\\"Which city has the highest population: Guangzhou or Shanghai?\\\"\\\\n\\\\nThought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.\\\\n\\\\nfor city in [\\\"Guangzhou\\\", \\\"Shanghai\\\"]:\\\\n print(f\\\"Population {city}:\\\", web_search(f\\\"{city} population\\\"))\\\\n\\\\nObservation:\\\\nPopulation Guangzhou: [\\\\'Guangzhou has a population of 15 million inhabitants as of 2021.\\\\']\\\\nPopulation Shanghai: \\\\'26 million (2019)\\\\'\\\\n\\\\nThought: Now I know that Shanghai has the highest population.\\\\n\\\\nfinal_answer(\\\"Shanghai\\\")\\\\n\\\\n\\\\n---\\\\nTask: \\\"What is the current age of the pope, raised to the power 0.36?\\\"\\\\n\\\\nThought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.\\\\n\\\\npope_age_wiki = wikipedia_search(query=\\\"current pope age\\\")\\\\nprint(\\\"Pope age as per wikipedia:\\\", pope_age_wiki)\\\\npope_age_search = web_search(query=\\\"current pope age\\\")\\\\nprint(\\\"Pope age as per google search:\\\", pope_age_search)\\\\n\\\\nObservation:\\\\nPope age: \\\"The pope Francis is currently 88 years old.\\\"\\\\n\\\\nThought: I know that the pope is 88 years old. Let\\\\'s compute the result using Python code.\\\\n\\\\npope_current_age = 88 ** 0.36\\\\nfinal_answer(pope_current_age)\\\\n\\\\n\\\\nAbove examples were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:\\\\n\\\\ndef web_search(query: string) -> string:\\\\n \\\"\\\"\\\"Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\\\\n\\\\n Args:\\\\n query: The search query to perform.\\\\n \\\"\\\"\\\"\\\\n\\\\ndef final_answer(answer: any) -> any:\\\\n \\\"\\\"\\\"Provides a final answer to the given problem.\\\\n\\\\n Args:\\\\n answer: The final answer to the problem\\\\n \\\"\\\"\\\"\\\\n\\\\n\\\\n\\\\nHere are the rules you should always follow to solve your task:\\\\n1. Always provide a \\\\'Thought:\\\\' sequence, and a \\\\'\\\\' sequence ending with \\\\'\\\\', else you will fail.\\\\n2. Use only variables that you have defined!\\\\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in \\\\'answer = wikipedia_search({\\\\'query\\\\': \\\"What is the place where James Bond lives?\\\"})\\\\', but use the arguments directly as in \\\\'answer = wikipedia_search(query=\\\"What is the place where James Bond lives?\\\")\\\\'.\\\\n4. For tools WITHOUT JSON output schema: Take care to not chain too many sequential tool calls in the same code block, as their output format is unpredictable. For instance, a call to wikipedia_search without a JSON output schema has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\\\\n5. For tools WITH JSON output schema: You can confidently chain multiple tool calls and directly access structured output fields in the same code block! When a tool has a JSON output schema, you know exactly what fields and data types to expect, allowing you to write robust code that directly accesses the structured response (e.g., result[\\\\'field_name\\\\']) without needing intermediate print() statements.\\\\n6. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\\\\n7. Don\\\\'t name any new variable with the same name as a tool: for instance don\\\\'t name a variable \\\\'final_answer\\\\'.\\\\n8. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\\\\n9. You can use imports in your code, but only from the following list of modules: [\\\\'collections\\\\', \\\\'datetime\\\\', \\\\'itertools\\\\', \\\\'math\\\\', \\\\'queue\\\\', \\\\'random\\\\', \\\\'re\\\\', \\\\'stat\\\\', \\\\'statistics\\\\', \\\\'time\\\\', \\\\'unicodedata\\\\']\\\\n10. The state persists between code executions: so if in one step you\\\\'ve created variables or imported modules, these will all persist.\\\\n11. Don\\\\'t give up! You\\\\'re in charge of solving the task, not providing directions to solve it.\\\\n\\\\nNow Begin!'}], tool_calls=None, raw=None, token_usage=None)\", \"ChatMessage(role=, content=[{'type': 'text', 'text': 'New task:\\\\nWhat is the population of Tokyo?'}], tool_calls=None, raw=None, token_usage=None)\"], \"stop_sequences\": [\"Observation:\", \"Calling tools:\", \"\"], \"response_format\": null, \"tools_to_call_from\": null, \"kwargs\": {}}", + "llm.invocation_parameters": "{}", + "llm.input_messages.0.message.role": "system", + "llm.input_messages.0.message.contents.0.message_content.type": "text", + "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.\n\nAt each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\nThen in the Code sequence you should write the code in simple Python. The code sequence must be opened with '', and closed with ''.\nDuring each intermediate step, you can use 'print()' to save whatever important information you will then need.\nThese print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\n\nanswer = document_qa(document=document, question=\"Who is the oldest person mentioned?\")\nprint(answer)\n\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nThought: I will now generate an image showcasing the oldest person.\n\nimage = image_generator(\"A portrait of John Doe, a 55-year-old man living in Canada.\")\nfinal_answer(image)\n\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nThought: I will use Python code to compute the result of the operation and then return the final answer using the `final_answer` tool.\n\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n\n\n---\nTask:\n\"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\nYou have been provided with these additional arguments, that you can access using the keys as variables in your Python code:\n{'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}\"\n\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\n\ntranslated_question = translator(question=question, src_lang=\"French\", tgt_lang=\"English\")\nprint(f\"The translated question is {translated_question}.\")\nanswer = image_qa(image=image, question=translated_question)\nfinal_answer(f\"The answer is {answer}\")\n\n\n---\nTask:\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\n\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\n\npages = web_search(query=\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\")\nprint(pages)\n\nObservation:\nNo result found for query \"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\".\n\nThought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.\n\npages = web_search(query=\"1979 interview Stanislaus Ulam\")\nprint(pages)\n\nObservation:\nFound 6 pages:\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\n\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\n\n(truncated)\n\nThought: I will read the first 2 pages to know more.\n\nfor url in [\"https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/\", \"https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/\"]:\n whole_page = visit_webpage(url)\n print(whole_page)\n print(\"\\n\" + \"=\"*80 + \"\\n\") # Print separator between pages\n\nObservation:\nManhattan Project Locations:\nLos Alamos, NM\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\n(truncated)\n\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: \"He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity.\" Let's answer in one word.\n\nfinal_answer(\"diminished\")\n\n\n---\nTask: \"Which city has the highest population: Guangzhou or Shanghai?\"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.\n\nfor city in [\"Guangzhou\", \"Shanghai\"]:\n print(f\"Population {city}:\", web_search(f\"{city} population\"))\n\nObservation:\nPopulation Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\nPopulation Shanghai: '26 million (2019)'\n\nThought: Now I know that Shanghai has the highest population.\n\nfinal_answer(\"Shanghai\")\n\n\n---\nTask: \"What is the current age of the pope, raised to the power 0.36?\"\n\nThought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.\n\npope_age_wiki = wikipedia_search(query=\"current pope age\")\nprint(\"Pope age as per wikipedia:\", pope_age_wiki)\npope_age_search = web_search(query=\"current pope age\")\nprint(\"Pope age as per google search:\", pope_age_search)\n\nObservation:\nPope age: \"The pope Francis is currently 88 years old.\"\n\nThought: I know that the pope is 88 years old. Let's compute the result using Python code.\n\npope_current_age = 88 ** 0.36\nfinal_answer(pope_current_age)\n\n\nAbove examples were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:\n\ndef web_search(query: string) -> string:\n \"\"\"Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\n\n Args:\n query: The search query to perform.\n \"\"\"\n\ndef final_answer(answer: any) -> any:\n \"\"\"Provides a final answer to the given problem.\n\n Args:\n answer: The final answer to the problem\n \"\"\"\n\n\n\nHere are the rules you should always follow to solve your task:\n1. Always provide a 'Thought:' sequence, and a '' sequence ending with '', else you will fail.\n2. Use only variables that you have defined!\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wikipedia_search({'query': \"What is the place where James Bond lives?\"})', but use the arguments directly as in 'answer = wikipedia_search(query=\"What is the place where James Bond lives?\")'.\n4. For tools WITHOUT JSON output schema: Take care to not chain too many sequential tool calls in the same code block, as their output format is unpredictable. For instance, a call to wikipedia_search without a JSON output schema has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\n5. For tools WITH JSON output schema: You can confidently chain multiple tool calls and directly access structured output fields in the same code block! When a tool has a JSON output schema, you know exactly what fields and data types to expect, allowing you to write robust code that directly accesses the structured response (e.g., result['field_name']) without needing intermediate print() statements.\n6. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\n7. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.\n8. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\n9. You can use imports in your code, but only from the following list of modules: ['collections', 'datetime', 'itertools', 'math', 'queue', 'random', 're', 'stat', 'statistics', 'time', 'unicodedata']\n10. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.\n11. Don't give up! You're in charge of solving the task, not providing directions to solve it.\n\nNow Begin!", + "llm.input_messages.1.message.role": "user", + "llm.input_messages.1.message.contents.0.message_content.type": "text", + "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat is the population of Tokyo?", + "llm.token_count.prompt": 2366, + "llm.token_count.completion": 52, + "llm.token_count.total": 2418, + "llm.model_name": "bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0", + "llm.output_messages.0.message.role": "assistant", + "llm.output_messages.0.message.contents.0.message_content.type": "text", + "llm.output_messages.0.message.contents.0.message_content.text": "Thought: I need to find the current population of Tokyo. I'll use the web_search tool to find this information.\n\nresult = web_search(query=\"population of Tokyo\")\nprint(result)\n", + "output.mime_type": "application/json", + "output.value": "{\"role\": \"assistant\", \"content\": \"Thought: I need to find the current population of Tokyo. I'll use the web_search tool to find this information.\\n\\nresult = web_search(query=\\\"population of Tokyo\\\")\\nprint(result)\\n\", \"tool_calls\": null, \"raw\": \"ModelResponse(id='chatcmpl-ba9a7fcd-29d0-4ac1-a5b4-0429d5cdf86e', created=1784230699, model='us.anthropic.claude-haiku-4-5-20251001-v1:0', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='Thought: I need to find the current population of Tokyo. I\\\\'ll use the web_search tool to find this information.\\\\n\\\\nresult = web_search(query=\\\"population of Tokyo\\\")\\\\nprint(result)\\\\n', role='assistant', tool_calls=None, function_call=None, provider_specific_fields=None))], usage=Usage(completion_tokens=52, prompt_tokens=2366, total_tokens=2418, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=0, rejected_prediction_tokens=None, text_tokens=52, image_tokens=None, video_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cache_write_tokens=None, cached_tokens=0, text_tokens=2366, image_tokens=None, video_tokens=None, cache_creation_tokens=0), cache_creation_input_tokens=0, cache_read_input_tokens=0))\", \"token_usage\": {\"input_tokens\": 2366, \"output_tokens\": 52, \"total_tokens\": 2418}}", + "openinference.span.kind": "LLM" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + }, + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "6f3d4f46c51e575f", + "parent_span_id": "11314f4c80c23993", + "name": "DuckDuckGoSearchTool", + "start_time": 1784230699180747272, + "end_time": 1784230699972102125, + "attributes": { + "input.value": "{\"args\": [], \"sanitize_inputs_outputs\": false, \"kwargs\": {\"query\": \"population of Tokyo\"}}", + "tool.name": "web_search", + "tool.description": "Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.", + "tool.parameters": "{\"query\": {\"type\": \"string\", \"description\": \"The search query to perform.\"}}", + "output.value": "## Search Results\n\n[Population of Tokelau](https://en.wikipedia.org/wiki/Population_of_Tokelau)\nTokelau ( ; lit. 'north-northeast' or 'north wind'; known previously as the Union Islands, and, until 1976, known officially as the Tokelau Islands) is a dependent territory of New Zealand in the southern Pacific Ocean. It consists of three tropical coral atolls: Atafu, Nukunonu, and Fakaofo. They have a combined land area of 10 km2 (4 sq mi). In addition to these three, Swains Island (Olohega), which forms part of the same archipelago, is the subject of an ongoing territorial dispute, while being currently administered by the United States as part of American Samoa. Tokelau lies north of the Samoan Islands, east of Tuvalu, south of the Phoenix Islands, southwest of the more distant Line Islands, and northwest of the Cook Islands. The islands sit in the south east hemisphere.Tokelau has a population of approximately 2,500 people; it has the fourth-smallest population of any sovereign state or dependency in the world. As of the 2016 census, around 45% of its residents had been born overseas, mostly in Samoa or New Zealand. The populace has a life expectancy of 69, which is comparable to that of other Oceanian island nations. Approximately 94% of the population speak Tokelauan as their first language. Tokelau has the smallest economy of any nation. It is a leader in renewable energy, being the first 100% solar-powered nation in the world.Tokelau is officially referred to as a nation by both the New Zealand government and the Tokelauan government. It is free and democratic, with elections every three years. However, in 2007, the United Nations General Assembly included Tokelau on its list of non-self-governing territories. Its inclusion on this list is controversial, as Tokelauans have twice narrowly failed to reach a two-thirds majority for further self-determination in referendums, and the islands' small population makes the viability of self-government challenging. The basis of Tokelau's legislative, administrative and judicial systems is the Tokelau Islands Act 1948, which has been amended several times. Since 1993, the territory has annually elected its own head of government, the Ulu-o-Tokelau. Before 1993, the administrator of Tokelau was the highest official in the government and the territory was directly administered by a New Zealand government department.\n\n[Tokyo - Wikipedia](https://en.wikipedia.org/wiki/Tokyo)\nTokyo, officially the Tokyo Metropolis, is the capital and most populous city of Japan. The population of the city proper was over 14 million as of 2023.\n\n[Demographics of Tokyo](https://grokipedia.com/page/demographics_of_tokyo)\nThe demographics of Tokyo encompass the population characteristics of the Tokyo Metropolis, a prefecture with approximately 14.0 million residents as of October 2023, accounting for 11.3% of Japan's total population and exhibiting a density of about\u2026\n\n[Tokyo seen from a plane, 41 million people live in the metropolitan ...](https://www.reddit.com/r/CityPorn/comments/1fyxy2p/tokyo_seen_from_a_plane_41_million_people_live_in/)\nOct 8, 2024 ... Tokyo seen from a plane, 41 million people live in the metropolitan area ... Fun fact: the urban area of Tokyo has about the same population as ...\n\n[Tokyo Population 2026](https://worldpopulationreview.com/cities/japan/tokyo)\nAccording to a 2016 estimate of Tokyo's population, the metropolis is now home to 13.5 million people, or 9,262,046 in all of the 23 wards. This does not ...\n\n[Tokyo Third in UN Ranking of Global Megacities at 33.4 Million](https://www.nippon.com/en/japan-data/h02639/)\nJan 15, 2026 ... According to the United Nations report Prospects for World Urbanization (2025 edition), the population of the Tokyo metropolitan area is 33.4 ...\n\n[Tokyo replaced as largest city in the world by population - YouTube](https://www.youtube.com/shorts/Llb1UM4kGCk)\nDec 5, 2025 ... For over a decade, the Japanese capital Tokyo has been the largest city by population. But this year, it's ranked third, while the top spot ...\n\n[Tokyo in one picture. Tokyo Metropolis is the capital and most ...](https://www.facebook.com/archpng/posts/tokyo-in-one-picturetokyo-metropolis-is-the-capital-and-most-populous-prefecture/885189454008133/)\nNov 28, 2025 ... The prefecture had an estimated population of 14 million. The Greater Tokyo Area is the most populous metropolitan area in the world, with more ...\n\n[Tokyo, Japan Metro Area Population (1950-2026) - Macrotrends](https://www.macrotrends.net/global-metrics/cities/21671/tokyo/population)\nThe current metro area population of Tokyo in 2026 is 36,954,000, a 0.22% decline from 2025. The metro area population of Tokyo in 2025 was 37,036,000, ...\n\n[Tokyo - statistics & facts - Statista](https://www.statista.com/topics/9914/tokyo/?srsltid=AfmBOorzfWoByPnkqhU8GNhtVOrEuCWbSGul_PLxyNKrVWegKOcjuSAr)\nIt is home to around 14 million people, making up over 10 percent of the nation's total population. Tokyo is not only the economic and cultural hub of Japan but ...", + "output.mime_type": "text/plain", + "openinference.span.kind": "TOOL" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + }, + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "11314f4c80c23993", + "parent_span_id": "93fcaaeb814a0650", + "name": "Step 1", + "start_time": 1784230697964132521, + "end_time": 1784230699972845324, + "attributes": { + "input.value": "{\"memory_step\": \"ActionStep(step_number=1, timing=Timing(start_time=1784230697.9639382, end_time=None, duration=None), model_input_messages=None, tool_calls=None, error=None, model_output_message=None, model_output=None, code_action=None, observations=None, observations_images=None, action_output=None, token_usage=None, is_final_answer=False)\"}", + "output.value": "Execution logs:\n## Search Results\n\n[Population of Tokelau](https://en.wikipedia.org/wiki/Population_of_Tokelau)\nTokelau ( ; lit. 'north-northeast' or 'north wind'; known previously as the Union Islands, and, until 1976, known officially as the Tokelau Islands) is a dependent territory of New Zealand in the southern Pacific Ocean. It consists of three tropical coral atolls: Atafu, Nukunonu, and Fakaofo. They have a combined land area of 10 km2 (4 sq mi). In addition to these three, Swains Island (Olohega), which forms part of the same archipelago, is the subject of an ongoing territorial dispute, while being currently administered by the United States as part of American Samoa. Tokelau lies north of the Samoan Islands, east of Tuvalu, south of the Phoenix Islands, southwest of the more distant Line Islands, and northwest of the Cook Islands. The islands sit in the south east hemisphere.Tokelau has a population of approximately 2,500 people; it has the fourth-smallest population of any sovereign state or dependency in the world. As of the 2016 census, around 45% of its residents had been born overseas, mostly in Samoa or New Zealand. The populace has a life expectancy of 69, which is comparable to that of other Oceanian island nations. Approximately 94% of the population speak Tokelauan as their first language. Tokelau has the smallest economy of any nation. It is a leader in renewable energy, being the first 100% solar-powered nation in the world.Tokelau is officially referred to as a nation by both the New Zealand government and the Tokelauan government. It is free and democratic, with elections every three years. However, in 2007, the United Nations General Assembly included Tokelau on its list of non-self-governing territories. Its inclusion on this list is controversial, as Tokelauans have twice narrowly failed to reach a two-thirds majority for further self-determination in referendums, and the islands' small population makes the viability of self-government challenging. The basis of Tokelau's legislative, administrative and judicial systems is the Tokelau Islands Act 1948, which has been amended several times. Since 1993, the territory has annually elected its own head of government, the Ulu-o-Tokelau. Before 1993, the administrator of Tokelau was the highest official in the government and the territory was directly administered by a New Zealand government department.\n\n[Tokyo - Wikipedia](https://en.wikipedia.org/wiki/Tokyo)\nTokyo, officially the Tokyo Metropolis, is the capital and most populous city of Japan. The population of the city proper was over 14 million as of 2023.\n\n[Demographics of Tokyo](https://grokipedia.com/page/demographics_of_tokyo)\nThe demographics of Tokyo encompass the population characteristics of the Tokyo Metropolis, a prefecture with approximately 14.0 million residents as of October 2023, accounting for 11.3% of Japan's total population and exhibiting a density of about\u2026\n\n[Tokyo seen from a plane, 41 million people live in the metropolitan ...](https://www.reddit.com/r/CityPorn/comments/1fyxy2p/tokyo_seen_from_a_plane_41_million_people_live_in/)\nOct 8, 2024 ... Tokyo seen from a plane, 41 million people live in the metropolitan area ... Fun fact: the urban area of Tokyo has about the same population as ...\n\n[Tokyo Population 2026](https://worldpopulationreview.com/cities/japan/tokyo)\nAccording to a 2016 estimate of Tokyo's population, the metropolis is now home to 13.5 million people, or 9,262,046 in all of the 23 wards. This does not ...\n\n[Tokyo Third in UN Ranking of Global Megacities at 33.4 Million](https://www.nippon.com/en/japan-data/h02639/)\nJan 15, 2026 ... According to the United Nations report Prospects for World Urbanization (2025 edition), the population of the Tokyo metropolitan area is 33.4 ...\n\n[Tokyo replaced as largest city in the world by population - YouTube](https://www.youtube.com/shorts/Llb1UM4kGCk)\nDec 5, 2025 ... For over a decade, the Japanese capital Tokyo has been the largest city by population. But this year, it's ranked third, while the top spot ...\n\n[Tokyo in one picture. Tokyo Metropolis is the capital and most ...](https://www.facebook.com/archpng/posts/tokyo-in-one-picturetokyo-metropolis-is-the-capital-and-most-populous-prefecture/885189454008133/)\nNov 28, 2025 ... The prefecture had an estimated population of 14 million. The Greater Tokyo Area is the most populous metropolitan area in the world, with more ...\n\n[Tokyo, Japan Metro Area Population (1950-2026) - Macrotrends](https://www.macrotrends.net/global-metrics/cities/21671/tokyo/population)\nThe current metro area population of Tokyo in 2026 is 36,954,000, a 0.22% decline from 2025. The metro area population of Tokyo in 2025 was 37,036,000, ...\n\n[Tokyo - statistics & facts - Statista](https://www.statista.com/topics/9914/tokyo/?srsltid=AfmBOorzfWoByPnkqhU8GNhtVOrEuCWbSGul_PLxyNKrVWegKOcjuSAr)\nIt is home to around 14 million people, making up over 10 percent of the nation's total population. Tokyo is not only the economic and cultural hub of Japan but ...\nLast output from code snippet:\nNone", + "openinference.span.kind": "CHAIN" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + }, + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "9f024ee562c898c8", + "parent_span_id": "d3587affad8a3568", + "name": "LiteLLMModel.generate", + "start_time": 1784230699973566982, + "end_time": 1784230702029302035, + "attributes": { + "input.mime_type": "application/json", + "input.value": "{\"messages\": [\"ChatMessage(role=, content=[{'type': 'text', 'text': 'You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\\\\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\\\\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.\\\\n\\\\nAt each step, in the \\\\'Thought:\\\\' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\\\\nThen in the Code sequence you should write the code in simple Python. The code sequence must be opened with \\\\'\\\\', and closed with \\\\'\\\\'.\\\\nDuring each intermediate step, you can use \\\\'print()\\\\' to save whatever important information you will then need.\\\\nThese print outputs will then appear in the \\\\'Observation:\\\\' field, which will be available as input for the next step.\\\\nIn the end you have to return a final answer using the `final_answer` tool.\\\\n\\\\nHere are a few examples using notional tools:\\\\n---\\\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\\\n\\\\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\\\\n\\\\nanswer = document_qa(document=document, question=\\\"Who is the oldest person mentioned?\\\")\\\\nprint(answer)\\\\n\\\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\\\n\\\\nThought: I will now generate an image showcasing the oldest person.\\\\n\\\\nimage = image_generator(\\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\")\\\\nfinal_answer(image)\\\\n\\\\n\\\\n---\\\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\\\n\\\\nThought: I will use Python code to compute the result of the operation and then return the final answer using the `final_answer` tool.\\\\n\\\\nresult = 5 + 3 + 1294.678\\\\nfinal_answer(result)\\\\n\\\\n\\\\n---\\\\nTask:\\\\n\\\"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\\\\nYou have been provided with these additional arguments, that you can access using the keys as variables in your Python code:\\\\n{\\\\'question\\\\': \\\\'Quel est l\\\\'animal sur l\\\\'image?\\\\', \\\\'image\\\\': \\\\'path/to/image.jpg\\\\'}\\\"\\\\n\\\\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\\\\n\\\\ntranslated_question = translator(question=question, src_lang=\\\"French\\\", tgt_lang=\\\"English\\\")\\\\nprint(f\\\"The translated question is {translated_question}.\\\")\\\\nanswer = image_qa(image=image, question=translated_question)\\\\nfinal_answer(f\\\"The answer is {answer}\\\")\\\\n\\\\n\\\\n---\\\\nTask:\\\\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\\\\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\\\\n\\\\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\\\\n\\\\npages = web_search(query=\\\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\\\")\\\\nprint(pages)\\\\n\\\\nObservation:\\\\nNo result found for query \\\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\\\".\\\\n\\\\nThought: The query was maybe too restrictive and did not find any results. Let\\\\'s try again with a broader query.\\\\n\\\\npages = web_search(query=\\\"1979 interview Stanislaus Ulam\\\")\\\\nprint(pages)\\\\n\\\\nObservation:\\\\nFound 6 pages:\\\\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\\\\n\\\\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\\\\n\\\\n(truncated)\\\\n\\\\nThought: I will read the first 2 pages to know more.\\\\n\\\\nfor url in [\\\"https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/\\\", \\\"https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/\\\"]:\\\\n whole_page = visit_webpage(url)\\\\n print(whole_page)\\\\n print(\\\"\\\\\\\\n\\\" + \\\"=\\\"*80 + \\\"\\\\\\\\n\\\") # Print separator between pages\\\\n\\\\nObservation:\\\\nManhattan Project Locations:\\\\nLos Alamos, NM\\\\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\\\\n(truncated)\\\\n\\\\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: \\\"He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity.\\\" Let\\\\'s answer in one word.\\\\n\\\\nfinal_answer(\\\"diminished\\\")\\\\n\\\\n\\\\n---\\\\nTask: \\\"Which city has the highest population: Guangzhou or Shanghai?\\\"\\\\n\\\\nThought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.\\\\n\\\\nfor city in [\\\"Guangzhou\\\", \\\"Shanghai\\\"]:\\\\n print(f\\\"Population {city}:\\\", web_search(f\\\"{city} population\\\"))\\\\n\\\\nObservation:\\\\nPopulation Guangzhou: [\\\\'Guangzhou has a population of 15 million inhabitants as of 2021.\\\\']\\\\nPopulation Shanghai: \\\\'26 million (2019)\\\\'\\\\n\\\\nThought: Now I know that Shanghai has the highest population.\\\\n\\\\nfinal_answer(\\\"Shanghai\\\")\\\\n\\\\n\\\\n---\\\\nTask: \\\"What is the current age of the pope, raised to the power 0.36?\\\"\\\\n\\\\nThought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.\\\\n\\\\npope_age_wiki = wikipedia_search(query=\\\"current pope age\\\")\\\\nprint(\\\"Pope age as per wikipedia:\\\", pope_age_wiki)\\\\npope_age_search = web_search(query=\\\"current pope age\\\")\\\\nprint(\\\"Pope age as per google search:\\\", pope_age_search)\\\\n\\\\nObservation:\\\\nPope age: \\\"The pope Francis is currently 88 years old.\\\"\\\\n\\\\nThought: I know that the pope is 88 years old. Let\\\\'s compute the result using Python code.\\\\n\\\\npope_current_age = 88 ** 0.36\\\\nfinal_answer(pope_current_age)\\\\n\\\\n\\\\nAbove examples were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:\\\\n\\\\ndef web_search(query: string) -> string:\\\\n \\\"\\\"\\\"Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\\\\n\\\\n Args:\\\\n query: The search query to perform.\\\\n \\\"\\\"\\\"\\\\n\\\\ndef final_answer(answer: any) -> any:\\\\n \\\"\\\"\\\"Provides a final answer to the given problem.\\\\n\\\\n Args:\\\\n answer: The final answer to the problem\\\\n \\\"\\\"\\\"\\\\n\\\\n\\\\n\\\\nHere are the rules you should always follow to solve your task:\\\\n1. Always provide a \\\\'Thought:\\\\' sequence, and a \\\\'\\\\' sequence ending with \\\\'\\\\', else you will fail.\\\\n2. Use only variables that you have defined!\\\\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in \\\\'answer = wikipedia_search({\\\\'query\\\\': \\\"What is the place where James Bond lives?\\\"})\\\\', but use the arguments directly as in \\\\'answer = wikipedia_search(query=\\\"What is the place where James Bond lives?\\\")\\\\'.\\\\n4. For tools WITHOUT JSON output schema: Take care to not chain too many sequential tool calls in the same code block, as their output format is unpredictable. For instance, a call to wikipedia_search without a JSON output schema has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\\\\n5. For tools WITH JSON output schema: You can confidently chain multiple tool calls and directly access structured output fields in the same code block! When a tool has a JSON output schema, you know exactly what fields and data types to expect, allowing you to write robust code that directly accesses the structured response (e.g., result[\\\\'field_name\\\\']) without needing intermediate print() statements.\\\\n6. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\\\\n7. Don\\\\'t name any new variable with the same name as a tool: for instance don\\\\'t name a variable \\\\'final_answer\\\\'.\\\\n8. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\\\\n9. You can use imports in your code, but only from the following list of modules: [\\\\'collections\\\\', \\\\'datetime\\\\', \\\\'itertools\\\\', \\\\'math\\\\', \\\\'queue\\\\', \\\\'random\\\\', \\\\'re\\\\', \\\\'stat\\\\', \\\\'statistics\\\\', \\\\'time\\\\', \\\\'unicodedata\\\\']\\\\n10. The state persists between code executions: so if in one step you\\\\'ve created variables or imported modules, these will all persist.\\\\n11. Don\\\\'t give up! You\\\\'re in charge of solving the task, not providing directions to solve it.\\\\n\\\\nNow Begin!'}], tool_calls=None, raw=None, token_usage=None)\", \"ChatMessage(role=, content=[{'type': 'text', 'text': 'New task:\\\\nWhat is the population of Tokyo?'}], tool_calls=None, raw=None, token_usage=None)\", \"ChatMessage(role=, content=[{'type': 'text', 'text': 'Thought: I need to find the current population of Tokyo. I\\\\'ll use the web_search tool to find this information.\\\\n\\\\nresult = web_search(query=\\\"population of Tokyo\\\")\\\\nprint(result)\\\\n'}], tool_calls=None, raw=None, token_usage=None)\", \"ChatMessage(role=, content=[{'type': 'text', 'text': 'Calling tools:\\\\n[{\\\\'id\\\\': \\\\'call_1\\\\', \\\\'type\\\\': \\\\'function\\\\', \\\\'function\\\\': {\\\\'name\\\\': \\\\'python_interpreter\\\\', \\\\'arguments\\\\': \\\\'result = web_search(query=\\\"population of Tokyo\\\")\\\\\\\\nprint(result)\\\\'}}]'}], tool_calls=None, raw=None, token_usage=None)\", \"ChatMessage(role=, content=[{'type': 'text', 'text': \\\"Observation:\\\\nExecution logs:\\\\n## Search Results\\\\n\\\\n[Population of Tokelau](https://en.wikipedia.org/wiki/Population_of_Tokelau)\\\\nTokelau ( ; lit. 'north-northeast' or 'north wind'; known previously as the Union Islands, and, until 1976, known officially as the Tokelau Islands) is a dependent territory of New Zealand in the southern Pacific Ocean. It consists of three tropical coral atolls: Atafu, Nukunonu, and Fakaofo. They have a combined land area of 10 km2 (4 sq mi). In addition to these three, Swains Island (Olohega), which forms part of the same archipelago, is the subject of an ongoing territorial dispute, while being currently administered by the United States as part of American Samoa. Tokelau lies north of the Samoan Islands, east of Tuvalu, south of the Phoenix Islands, southwest of the more distant Line Islands, and northwest of the Cook Islands. The islands sit in the south east hemisphere.Tokelau has a population of approximately 2,500 people; it has the fourth-smallest population of any sovereign state or dependency in the world. As of the 2016 census, around 45% of its residents had been born overseas, mostly in Samoa or New Zealand. The populace has a life expectancy of 69, which is comparable to that of other Oceanian island nations. Approximately 94% of the population speak Tokelauan as their first language. Tokelau has the smallest economy of any nation. It is a leader in renewable energy, being the first 100% solar-powered nation in the world.Tokelau is officially referred to as a nation by both the New Zealand government and the Tokelauan government. It is free and democratic, with elections every three years. However, in 2007, the United Nations General Assembly included Tokelau on its list of non-self-governing territories. Its inclusion on this list is controversial, as Tokelauans have twice narrowly failed to reach a two-thirds majority for further self-determination in referendums, and the islands' small population makes the viability of self-government challenging. The basis of Tokelau's legislative, administrative and judicial systems is the Tokelau Islands Act 1948, which has been amended several times. Since 1993, the territory has annually elected its own head of government, the Ulu-o-Tokelau. Before 1993, the administrator of Tokelau was the highest official in the government and the territory was directly administered by a New Zealand government department.\\\\n\\\\n[Tokyo - Wikipedia](https://en.wikipedia.org/wiki/Tokyo)\\\\nTokyo, officially the Tokyo Metropolis, is the capital and most populous city of Japan. The population of the city proper was over 14 million as of 2023.\\\\n\\\\n[Demographics of Tokyo](https://grokipedia.com/page/demographics_of_tokyo)\\\\nThe demographics of Tokyo encompass the population characteristics of the Tokyo Metropolis, a prefecture with approximately 14.0 million residents as of October 2023, accounting for 11.3% of Japan's total population and exhibiting a density of about\u2026\\\\n\\\\n[Tokyo seen from a plane, 41 million people live in the metropolitan ...](https://www.reddit.com/r/CityPorn/comments/1fyxy2p/tokyo_seen_from_a_plane_41_million_people_live_in/)\\\\nOct 8, 2024 ... Tokyo seen from a plane, 41 million people live in the metropolitan area ... Fun fact: the urban area of Tokyo has about the same population as ...\\\\n\\\\n[Tokyo Population 2026](https://worldpopulationreview.com/cities/japan/tokyo)\\\\nAccording to a 2016 estimate of Tokyo's population, the metropolis is now home to 13.5 million people, or 9,262,046 in all of the 23 wards. This does not ...\\\\n\\\\n[Tokyo Third in UN Ranking of Global Megacities at 33.4 Million](https://www.nippon.com/en/japan-data/h02639/)\\\\nJan 15, 2026 ... According to the United Nations report Prospects for World Urbanization (2025 edition), the population of the Tokyo metropolitan area is 33.4 ...\\\\n\\\\n[Tokyo replaced as largest city in the world by population - YouTube](https://www.youtube.com/shorts/Llb1UM4kGCk)\\\\nDec 5, 2025 ... For over a decade, the Japanese capital Tokyo has been the largest city by population. But this year, it's ranked third, while the top spot ...\\\\n\\\\n[Tokyo in one picture. Tokyo Metropolis is the capital and most ...](https://www.facebook.com/archpng/posts/tokyo-in-one-picturetokyo-metropolis-is-the-capital-and-most-populous-prefecture/885189454008133/)\\\\nNov 28, 2025 ... The prefecture had an estimated population of 14 million. The Greater Tokyo Area is the most populous metropolitan area in the world, with more ...\\\\n\\\\n[Tokyo, Japan Metro Area Population (1950-2026) - Macrotrends](https://www.macrotrends.net/global-metrics/cities/21671/tokyo/population)\\\\nThe current metro area population of Tokyo in 2026 is 36,954,000, a 0.22% decline from 2025. The metro area population of Tokyo in 2025 was 37,036,000, ...\\\\n\\\\n[Tokyo - statistics & facts - Statista](https://www.statista.com/topics/9914/tokyo/?srsltid=AfmBOorzfWoByPnkqhU8GNhtVOrEuCWbSGul_PLxyNKrVWegKOcjuSAr)\\\\nIt is home to around 14 million people, making up over 10 percent of the nation's total population. Tokyo is not only the economic and cultural hub of Japan but ...\\\\nLast output from code snippet:\\\\nNone\\\"}], tool_calls=None, raw=None, token_usage=None)\"], \"stop_sequences\": [\"Observation:\", \"Calling tools:\", \"\"], \"response_format\": null, \"tools_to_call_from\": null, \"kwargs\": {}}", + "llm.invocation_parameters": "{}", + "llm.input_messages.0.message.role": "system", + "llm.input_messages.0.message.contents.0.message_content.type": "text", + "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.\n\nAt each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\nThen in the Code sequence you should write the code in simple Python. The code sequence must be opened with '', and closed with ''.\nDuring each intermediate step, you can use 'print()' to save whatever important information you will then need.\nThese print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\n\nanswer = document_qa(document=document, question=\"Who is the oldest person mentioned?\")\nprint(answer)\n\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nThought: I will now generate an image showcasing the oldest person.\n\nimage = image_generator(\"A portrait of John Doe, a 55-year-old man living in Canada.\")\nfinal_answer(image)\n\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nThought: I will use Python code to compute the result of the operation and then return the final answer using the `final_answer` tool.\n\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n\n\n---\nTask:\n\"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\nYou have been provided with these additional arguments, that you can access using the keys as variables in your Python code:\n{'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}\"\n\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\n\ntranslated_question = translator(question=question, src_lang=\"French\", tgt_lang=\"English\")\nprint(f\"The translated question is {translated_question}.\")\nanswer = image_qa(image=image, question=translated_question)\nfinal_answer(f\"The answer is {answer}\")\n\n\n---\nTask:\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\n\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\n\npages = web_search(query=\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\")\nprint(pages)\n\nObservation:\nNo result found for query \"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\".\n\nThought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.\n\npages = web_search(query=\"1979 interview Stanislaus Ulam\")\nprint(pages)\n\nObservation:\nFound 6 pages:\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\n\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\n\n(truncated)\n\nThought: I will read the first 2 pages to know more.\n\nfor url in [\"https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/\", \"https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/\"]:\n whole_page = visit_webpage(url)\n print(whole_page)\n print(\"\\n\" + \"=\"*80 + \"\\n\") # Print separator between pages\n\nObservation:\nManhattan Project Locations:\nLos Alamos, NM\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\n(truncated)\n\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: \"He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity.\" Let's answer in one word.\n\nfinal_answer(\"diminished\")\n\n\n---\nTask: \"Which city has the highest population: Guangzhou or Shanghai?\"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.\n\nfor city in [\"Guangzhou\", \"Shanghai\"]:\n print(f\"Population {city}:\", web_search(f\"{city} population\"))\n\nObservation:\nPopulation Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\nPopulation Shanghai: '26 million (2019)'\n\nThought: Now I know that Shanghai has the highest population.\n\nfinal_answer(\"Shanghai\")\n\n\n---\nTask: \"What is the current age of the pope, raised to the power 0.36?\"\n\nThought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.\n\npope_age_wiki = wikipedia_search(query=\"current pope age\")\nprint(\"Pope age as per wikipedia:\", pope_age_wiki)\npope_age_search = web_search(query=\"current pope age\")\nprint(\"Pope age as per google search:\", pope_age_search)\n\nObservation:\nPope age: \"The pope Francis is currently 88 years old.\"\n\nThought: I know that the pope is 88 years old. Let's compute the result using Python code.\n\npope_current_age = 88 ** 0.36\nfinal_answer(pope_current_age)\n\n\nAbove examples were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:\n\ndef web_search(query: string) -> string:\n \"\"\"Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\n\n Args:\n query: The search query to perform.\n \"\"\"\n\ndef final_answer(answer: any) -> any:\n \"\"\"Provides a final answer to the given problem.\n\n Args:\n answer: The final answer to the problem\n \"\"\"\n\n\n\nHere are the rules you should always follow to solve your task:\n1. Always provide a 'Thought:' sequence, and a '' sequence ending with '', else you will fail.\n2. Use only variables that you have defined!\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wikipedia_search({'query': \"What is the place where James Bond lives?\"})', but use the arguments directly as in 'answer = wikipedia_search(query=\"What is the place where James Bond lives?\")'.\n4. For tools WITHOUT JSON output schema: Take care to not chain too many sequential tool calls in the same code block, as their output format is unpredictable. For instance, a call to wikipedia_search without a JSON output schema has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\n5. For tools WITH JSON output schema: You can confidently chain multiple tool calls and directly access structured output fields in the same code block! When a tool has a JSON output schema, you know exactly what fields and data types to expect, allowing you to write robust code that directly accesses the structured response (e.g., result['field_name']) without needing intermediate print() statements.\n6. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\n7. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.\n8. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\n9. You can use imports in your code, but only from the following list of modules: ['collections', 'datetime', 'itertools', 'math', 'queue', 'random', 're', 'stat', 'statistics', 'time', 'unicodedata']\n10. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.\n11. Don't give up! You're in charge of solving the task, not providing directions to solve it.\n\nNow Begin!", + "llm.input_messages.1.message.role": "user", + "llm.input_messages.1.message.contents.0.message_content.type": "text", + "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat is the population of Tokyo?", + "llm.input_messages.2.message.role": "assistant", + "llm.input_messages.2.message.contents.0.message_content.type": "text", + "llm.input_messages.2.message.contents.0.message_content.text": "Thought: I need to find the current population of Tokyo. I'll use the web_search tool to find this information.\n\nresult = web_search(query=\"population of Tokyo\")\nprint(result)\n", + "llm.input_messages.3.message.role": "tool-call", + "llm.input_messages.3.message.contents.0.message_content.type": "text", + "llm.input_messages.3.message.contents.0.message_content.text": "Calling tools:\n[{'id': 'call_1', 'type': 'function', 'function': {'name': 'python_interpreter', 'arguments': 'result = web_search(query=\"population of Tokyo\")\\nprint(result)'}}]", + "llm.input_messages.4.message.role": "tool-response", + "llm.input_messages.4.message.contents.0.message_content.type": "text", + "llm.input_messages.4.message.contents.0.message_content.text": "Observation:\nExecution logs:\n## Search Results\n\n[Population of Tokelau](https://en.wikipedia.org/wiki/Population_of_Tokelau)\nTokelau ( ; lit. 'north-northeast' or 'north wind'; known previously as the Union Islands, and, until 1976, known officially as the Tokelau Islands) is a dependent territory of New Zealand in the southern Pacific Ocean. It consists of three tropical coral atolls: Atafu, Nukunonu, and Fakaofo. They have a combined land area of 10 km2 (4 sq mi). In addition to these three, Swains Island (Olohega), which forms part of the same archipelago, is the subject of an ongoing territorial dispute, while being currently administered by the United States as part of American Samoa. Tokelau lies north of the Samoan Islands, east of Tuvalu, south of the Phoenix Islands, southwest of the more distant Line Islands, and northwest of the Cook Islands. The islands sit in the south east hemisphere.Tokelau has a population of approximately 2,500 people; it has the fourth-smallest population of any sovereign state or dependency in the world. As of the 2016 census, around 45% of its residents had been born overseas, mostly in Samoa or New Zealand. The populace has a life expectancy of 69, which is comparable to that of other Oceanian island nations. Approximately 94% of the population speak Tokelauan as their first language. Tokelau has the smallest economy of any nation. It is a leader in renewable energy, being the first 100% solar-powered nation in the world.Tokelau is officially referred to as a nation by both the New Zealand government and the Tokelauan government. It is free and democratic, with elections every three years. However, in 2007, the United Nations General Assembly included Tokelau on its list of non-self-governing territories. Its inclusion on this list is controversial, as Tokelauans have twice narrowly failed to reach a two-thirds majority for further self-determination in referendums, and the islands' small population makes the viability of self-government challenging. The basis of Tokelau's legislative, administrative and judicial systems is the Tokelau Islands Act 1948, which has been amended several times. Since 1993, the territory has annually elected its own head of government, the Ulu-o-Tokelau. Before 1993, the administrator of Tokelau was the highest official in the government and the territory was directly administered by a New Zealand government department.\n\n[Tokyo - Wikipedia](https://en.wikipedia.org/wiki/Tokyo)\nTokyo, officially the Tokyo Metropolis, is the capital and most populous city of Japan. The population of the city proper was over 14 million as of 2023.\n\n[Demographics of Tokyo](https://grokipedia.com/page/demographics_of_tokyo)\nThe demographics of Tokyo encompass the population characteristics of the Tokyo Metropolis, a prefecture with approximately 14.0 million residents as of October 2023, accounting for 11.3% of Japan's total population and exhibiting a density of about\u2026\n\n[Tokyo seen from a plane, 41 million people live in the metropolitan ...](https://www.reddit.com/r/CityPorn/comments/1fyxy2p/tokyo_seen_from_a_plane_41_million_people_live_in/)\nOct 8, 2024 ... Tokyo seen from a plane, 41 million people live in the metropolitan area ... Fun fact: the urban area of Tokyo has about the same population as ...\n\n[Tokyo Population 2026](https://worldpopulationreview.com/cities/japan/tokyo)\nAccording to a 2016 estimate of Tokyo's population, the metropolis is now home to 13.5 million people, or 9,262,046 in all of the 23 wards. This does not ...\n\n[Tokyo Third in UN Ranking of Global Megacities at 33.4 Million](https://www.nippon.com/en/japan-data/h02639/)\nJan 15, 2026 ... According to the United Nations report Prospects for World Urbanization (2025 edition), the population of the Tokyo metropolitan area is 33.4 ...\n\n[Tokyo replaced as largest city in the world by population - YouTube](https://www.youtube.com/shorts/Llb1UM4kGCk)\nDec 5, 2025 ... For over a decade, the Japanese capital Tokyo has been the largest city by population. But this year, it's ranked third, while the top spot ...\n\n[Tokyo in one picture. Tokyo Metropolis is the capital and most ...](https://www.facebook.com/archpng/posts/tokyo-in-one-picturetokyo-metropolis-is-the-capital-and-most-populous-prefecture/885189454008133/)\nNov 28, 2025 ... The prefecture had an estimated population of 14 million. The Greater Tokyo Area is the most populous metropolitan area in the world, with more ...\n\n[Tokyo, Japan Metro Area Population (1950-2026) - Macrotrends](https://www.macrotrends.net/global-metrics/cities/21671/tokyo/population)\nThe current metro area population of Tokyo in 2026 is 36,954,000, a 0.22% decline from 2025. The metro area population of Tokyo in 2025 was 37,036,000, ...\n\n[Tokyo - statistics & facts - Statista](https://www.statista.com/topics/9914/tokyo/?srsltid=AfmBOorzfWoByPnkqhU8GNhtVOrEuCWbSGul_PLxyNKrVWegKOcjuSAr)\nIt is home to around 14 million people, making up over 10 percent of the nation's total population. Tokyo is not only the economic and cultural hub of Japan but ...\nLast output from code snippet:\nNone", + "llm.token_count.prompt": 3890, + "llm.token_count.completion": 111, + "llm.token_count.total": 4001, + "llm.model_name": "bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0", + "llm.output_messages.0.message.role": "assistant", + "llm.output_messages.0.message.contents.0.message_content.type": "text", + "llm.output_messages.0.message.contents.0.message_content.text": "Thought: Based on the search results, I have found clear information about Tokyo's population. The search results show multiple sources confirming that Tokyo has a population of approximately 14 million people as of 2023. Let me provide this as the final answer.\n\nfinal_answer(\"The population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)\")\n", + "output.mime_type": "application/json", + "output.value": "{\"role\": \"assistant\", \"content\": \"Thought: Based on the search results, I have found clear information about Tokyo's population. The search results show multiple sources confirming that Tokyo has a population of approximately 14 million people as of 2023. Let me provide this as the final answer.\\n\\nfinal_answer(\\\"The population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)\\\")\\n\", \"tool_calls\": null, \"raw\": \"ModelResponse(id='chatcmpl-1e3f7f97-f3fe-4a73-b194-471f17b3b395', created=1784230702, model='us.anthropic.claude-haiku-4-5-20251001-v1:0', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='Thought: Based on the search results, I have found clear information about Tokyo\\\\'s population. The search results show multiple sources confirming that Tokyo has a population of approximately 14 million people as of 2023. Let me provide this as the final answer.\\\\n\\\\nfinal_answer(\\\"The population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)\\\")\\\\n', role='assistant', tool_calls=None, function_call=None, provider_specific_fields=None))], usage=Usage(completion_tokens=111, prompt_tokens=3890, total_tokens=4001, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=0, rejected_prediction_tokens=None, text_tokens=111, image_tokens=None, video_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cache_write_tokens=None, cached_tokens=0, text_tokens=3890, image_tokens=None, video_tokens=None, cache_creation_tokens=0), cache_creation_input_tokens=0, cache_read_input_tokens=0))\", \"token_usage\": {\"input_tokens\": 3890, \"output_tokens\": 111, \"total_tokens\": 4001}}", + "openinference.span.kind": "LLM" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + }, + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "22dbd2485281e912", + "parent_span_id": "d3587affad8a3568", + "name": "FinalAnswerTool", + "start_time": 1784230702030124626, + "end_time": 1784230702030210818, + "attributes": { + "input.value": "{\"args\": [\"The population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)\"], \"sanitize_inputs_outputs\": false, \"kwargs\": {}}", + "tool.name": "final_answer", + "tool.description": "Provides a final answer to the given problem.", + "tool.parameters": "{\"answer\": {\"type\": \"any\", \"description\": \"The final answer to the problem\"}}", + "output.value": "The population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)", + "output.mime_type": "text/plain", + "openinference.span.kind": "TOOL" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + }, + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "d3587affad8a3568", + "parent_span_id": "93fcaaeb814a0650", + "name": "Step 2", + "start_time": 1784230699973132261, + "end_time": 1784230702030490415, + "attributes": { + "input.value": "{\"memory_step\": \"ActionStep(step_number=2, timing=Timing(start_time=1784230699.9729474, end_time=None, duration=None), model_input_messages=None, tool_calls=None, error=None, model_output_message=None, model_output=None, code_action=None, observations=None, observations_images=None, action_output=None, token_usage=None, is_final_answer=False)\"}", + "output.value": "Execution logs:\nLast output from code snippet:\nThe population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)", + "openinference.span.kind": "CHAIN" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + }, + { + "trace_id": "f63de90470d666fccdf71a2b9cb7d089", + "span_id": "93fcaaeb814a0650", + "parent_span_id": null, + "name": "CodeAgent.run", + "start_time": 1784230697959364620, + "end_time": 1784230702030658600, + "attributes": { + "input.value": "{\"task\": \"What is the population of Tokyo?\", \"stream\": false, \"reset\": true, \"images\": null, \"additional_args\": null, \"max_steps\": null, \"return_full_result\": null}", + "smolagents.max_steps": 2, + "smolagents.tools_names": [ + "web_search", + "final_answer" + ], + "output.value": "The population of Tokyo is approximately 14 million people as of 2023. (The city proper has over 14 million residents, while the greater Tokyo metropolitan area has approximately 37 million people.)", + "llm.token_count.prompt": 6256, + "llm.token_count.completion": 163, + "llm.token_count.total": 6419, + "openinference.span.kind": "AGENT" + }, + "scope": { + "name": "openinference.instrumentation.smolagents", + "version": "0.1.31" + }, + "status": { + "code": "OK" + }, + "span_events": [] + } +] \ No newline at end of file diff --git a/tests/strands_evals/mappers/test_openinference_session_mapper.py b/tests/strands_evals/mappers/test_openinference_session_mapper.py index 36835507..5b42da60 100644 --- a/tests/strands_evals/mappers/test_openinference_session_mapper.py +++ b/tests/strands_evals/mappers/test_openinference_session_mapper.py @@ -17,8 +17,10 @@ _FIXTURES_DIR = Path(__file__).resolve().parent / "fixtures" _LIVE_SPANS_FILE = _FIXTURES_DIR / "openinference_live_spans.json" _ADOT_SPANS_FILE = _FIXTURES_DIR / "openinference_adot_spans.json" +_SMOLAGENTS_SPANS_FILE = _FIXTURES_DIR / "smolagents_live_spans.json" SCOPE_NAME = "openinference.instrumentation.langchain" +SMOLAGENTS_SCOPE_NAME = "openinference.instrumentation.smolagents" def make_span( @@ -30,6 +32,7 @@ def make_span( span_events=None, start_time=1700000000000000000, end_time=1700000001000000000, + scope_name=None, ): """Build a normalized span dict for OpenInference LangChain traces.""" return { @@ -40,7 +43,7 @@ def make_span( "start_time": start_time, "end_time": end_time, "attributes": attributes or {}, - "scope": {"name": SCOPE_NAME, "version": "0.1.0"}, + "scope": {"name": scope_name or SCOPE_NAME, "version": "0.1.0"}, "status": {"code": "OK"}, "span_events": span_events or [], } @@ -52,6 +55,7 @@ def make_llm_span( user_content="Hello", assistant_content="Hi there", tool_calls=None, + scope_name=None, ): """Build an LLM inference span with openinference attributes.""" attrs = { @@ -70,7 +74,7 @@ def make_llm_span( ) attrs[f"llm.output_messages.0.message.tool_calls.{i}.tool_call.id"] = tc.get("id", f"tool-{i}") - return make_span(trace_id=trace_id, span_id=span_id, attributes=attrs) + return make_span(trace_id=trace_id, span_id=span_id, attributes=attrs, scope_name=scope_name) def make_tool_span( @@ -80,6 +84,7 @@ def make_tool_span( tool_input=None, tool_output=None, tool_call_id="tool-1", + scope_name=None, ): """Build a tool execution span with openinference attributes.""" tool_input = tool_input or {"expr": "2+2"} @@ -100,7 +105,7 @@ def make_tool_span( "output.value": output_value, } - return make_span(trace_id=trace_id, span_id=span_id, name=tool_name, attributes=attrs) + return make_span(trace_id=trace_id, span_id=span_id, name=tool_name, attributes=attrs, scope_name=scope_name) def make_chain_span( @@ -176,6 +181,12 @@ def _load_adot_spans(): return json.load(f) +def _load_smolagents_spans(): + """Load real smolagents (openinference-instrumentation-smolagents) spans from fixture file.""" + with open(_SMOLAGENTS_SPANS_FILE) as f: + return json.load(f) + + class TestSpanTypeDetection: def setup_method(self): self.mapper = OpenInferenceSessionMapper() @@ -1349,3 +1360,239 @@ def test_no_empty_response_inference_spans(self, adot_session): text_only = all(hasattr(c, "text") for c in assistant_content) if text_only: assert any(c.text for c in assistant_content) + + +# ============================================================================= +# Smolagents Scope Support (regression: openinference.instrumentation.smolagents) +# +# smolagents (and the OpenInference semantic conventions it follows) sets +# output.value as a bare string rather than a JSON object with a "content" key, +# and uses a distinct scope name ("openinference.instrumentation.smolagents") +# that differs from the LangChain variant. These tests verify the mapper +# accepts this scope and correctly parses the bare-string output format. +# ============================================================================= + + +class TestSmolagentsScopeSupport: + """Smolagents-scoped spans must be accepted and correctly normalized.""" + + def setup_method(self): + self.mapper = OpenInferenceSessionMapper() + + def test_smolagents_tool_span(self): + """TOOL span with smolagents scope produces ToolExecutionSpan with correct fields.""" + span = make_tool_span( + tool_name="web_search", + tool_input={"query": "weather london"}, + tool_output="Temperature: 15C, cloudy", + scope_name=SMOLAGENTS_SCOPE_NAME, + ) + session = self.mapper.map_to_session([span], "sess-1") + + tool = session.traces[0].spans[0] + assert isinstance(tool, ToolExecutionSpan) + assert tool.tool_call.name == "web_search" + assert tool.tool_call.arguments == {"query": "weather london"} + assert tool.tool_result.content == "Temperature: 15C, cloudy" + + def test_smolagents_llm_span(self): + """LLM span with smolagents scope produces InferenceSpan.""" + span = make_llm_span( + user_content="What is 2+2?", + assistant_content="4", + scope_name=SMOLAGENTS_SCOPE_NAME, + ) + session = self.mapper.map_to_session([span], "sess-1") + assert isinstance(session.traces[0].spans[0], InferenceSpan) + + @pytest.mark.parametrize( + "output_value,expected_content", + [("42", "42"), ("[1, 2, 3]", "[1, 2, 3]"), ('"quoted"', '"quoted"')], + ids=["number", "list", "quoted-string"], + ) + def test_non_dict_json_output_not_crash(self, output_value, expected_content): + """TOOL span with non-dict JSON output.value must not crash.""" + span = make_span( + name="compute_tool", + scope_name=SMOLAGENTS_SCOPE_NAME, + attributes={ + "openinference.span.kind": "TOOL", + "tool.name": "compute_tool", + "input.value": json.dumps({"x": 1}), + "output.value": output_value, + }, + ) + session = self.mapper.map_to_session([span], "sess-1") + tool = session.traces[0].spans[0] + assert isinstance(tool, ToolExecutionSpan) + assert tool.tool_result.content == expected_content + + def test_mixed_positional_and_kwargs_merged(self): + """Positional args mapped via tool.parameters and merged with kwargs. + + {"args": ["tokyo", 5], "kwargs": {"offset": 10}} + params [query, limit, offset] + → {"query": "tokyo", "limit": 5, "offset": 10} + """ + span = make_span( + name="search", + scope_name=SMOLAGENTS_SCOPE_NAME, + attributes={ + "openinference.span.kind": "TOOL", + "tool.name": "search", + "tool.parameters": json.dumps({ + "query": {"type": "string"}, + "limit": {"type": "integer"}, + "offset": {"type": "integer"}, + }), + "input.value": json.dumps({ + "args": ["tokyo", 5], + "kwargs": {"offset": 10}, + "sanitize_inputs_outputs": False, + }), + "output.value": "results", + }, + ) + session = self.mapper.map_to_session([span], "sess-1") + tool = session.traces[0].spans[0] + assert tool.tool_call.arguments == {"query": "tokyo", "limit": 5, "offset": 10} + + def test_langchain_agent_span_rejected(self): + """LangChain AGENT spans (e.g. route_to_agent) with input+output explicitly rejected.""" + span = make_span( + name="route_to_agent", + scope_name="openinference.instrumentation.langchain", + attributes={ + "openinference.span.kind": "AGENT", + "input.value": '{"agent_name": "research_agent"}', + "output.value": '{"result": "done"}', + }, + ) + session = self.mapper.map_to_session([span], "sess-1") + agent_spans = [s for t in session.traces for s in t.spans if isinstance(s, AgentInvocationSpan)] + assert len(agent_spans) == 0 + + def test_agent_span_extracts_task_from_json_wrapper(self): + """AGENT span input.value JSON wrapper is parsed to extract just the 'task' field. + + Smolagents CodeAgent.run emits input.value as: + {"task": "What is 2+2?", "stream": false, "reset": true, ...} + The mapper must return only the task string, not the full JSON wrapper. + """ + span = make_span( + name="CodeAgent.run", + scope_name=SMOLAGENTS_SCOPE_NAME, + attributes={ + "openinference.span.kind": "AGENT", + "input.value": json.dumps({ + "task": "What is 2+2?", + "stream": False, + "reset": True, + "images": None, + "additional_args": None, + }), + "output.value": "The answer is 4.", + }, + ) + session = self.mapper.map_to_session([span], "sess-1") + all_spans = [s for t in session.traces for s in t.spans] + agent_spans = [s for s in all_spans if isinstance(s, AgentInvocationSpan)] + assert len(agent_spans) == 1 + assert agent_spans[0].user_prompt == "What is 2+2?" + + +# ============================================================================= +# Integration Tests: Real smolagents fixture +# (captured from openinference-instrumentation-smolagents v0.1.31, +# smolagents CodeAgent + DuckDuckGoSearchTool, scope +# "openinference.instrumentation.smolagents") +# +# These tests use ACTUAL spans produced by the instrumentor, validating that the +# mapper handles real-world encoding: +# - LLM spans with plural `contents` path (message.contents.0.message_content.text) +# - TOOL spans with wrapper input.value ({"args":[], "kwargs":{...}, ...}) +# - TOOL spans with bare-string output.value +# - Root AGENT span (CodeAgent.run) with input.value (task) and output.value (answer) +# ============================================================================= + + +@pytest.fixture(scope="module") +def smolagents_session(): + """Map real smolagents spans to a Session.""" + spans = _load_smolagents_spans() + mapper = OpenInferenceSessionMapper() + return mapper.map_to_session(spans, "smolagents-sess") + + +class TestSmolagentsFixtureIntegration: + """Integration tests using real smolagents trace (CodeAgent + DuckDuckGoSearchTool).""" + + def test_session_has_traces(self, smolagents_session): + """Smolagents fixture produces at least one trace.""" + assert len(smolagents_session.traces) >= 1 + + def test_produces_expected_span_types(self, smolagents_session): + """Real trace produces InferenceSpan, ToolExecutionSpan, and AgentInvocationSpan.""" + all_spans = [s for t in smolagents_session.traces for s in t.spans] + span_types = {type(s) for s in all_spans} + assert InferenceSpan in span_types + assert ToolExecutionSpan in span_types + assert AgentInvocationSpan in span_types + + def test_plural_contents_path_normalized(self, smolagents_session): + """LLM spans with plural contents path produce InferenceSpans with user+assistant.""" + all_spans = [s for t in smolagents_session.traces for s in t.spans] + inference_spans = [s for s in all_spans if isinstance(s, InferenceSpan)] + assert len(inference_spans) >= 1 + for span in inference_spans: + assert span.messages[0].role.value == "user" + assert span.messages[1].role.value == "assistant" + + def test_tool_spans_kwargs_normalized(self, smolagents_session): + """web_search: kwargs wrapper normalized to logical {"query": "..."}.""" + all_spans = [s for t in smolagents_session.traces for s in t.spans] + tool_spans = [s for s in all_spans if isinstance(s, ToolExecutionSpan)] + ws = next(s for s in tool_spans if s.tool_call.name == "web_search") + assert ws.tool_call.arguments == {"query": "population of Tokyo"} + + def test_tool_spans_positional_args_mapped(self, smolagents_session): + """FinalAnswerTool: positional arg mapped to "answer" via tool.parameters. + + {"args": ["...answer text..."], "kwargs": {}} → {"answer": "...answer text..."} + """ + all_spans = [s for t in smolagents_session.traces for s in t.spans] + tool_spans = [s for s in all_spans if isinstance(s, ToolExecutionSpan)] + fa = next(s for s in tool_spans if s.tool_call.name == "final_answer") + + expected_answer = ( + "The population of Tokyo is approximately 14 million people as of 2023. " + "(The city proper has over 14 million residents, while the greater Tokyo " + "metropolitan area has approximately 37 million people.)" + ) + assert fa.tool_call.arguments == {"answer": expected_answer} + assert fa.tool_call.arguments["answer"] == fa.tool_result.content + + def test_agent_span_extracts_task_field(self, smolagents_session): + """AGENT span: parses {"task": "...", "stream": ...} → user_prompt = task value.""" + all_spans = [s for t in smolagents_session.traces for s in t.spans] + agent_spans = [s for s in all_spans if isinstance(s, AgentInvocationSpan)] + assert len(agent_spans) == 1 + + agent = agent_spans[0] + assert agent.user_prompt == "What is the population of Tokyo?" + assert "population" in agent.agent_response.lower() + + def test_one_agent_span_per_trace(self, smolagents_session): + """Each trace has at most 1 AgentInvocationSpan.""" + for trace in smolagents_session.traces: + agent_spans = [s for s in trace.spans if isinstance(s, AgentInvocationSpan)] + assert len(agent_spans) <= 1 + + def test_no_empty_response_inference_spans(self, smolagents_session): + """No InferenceSpan has empty-text-only assistant content.""" + all_spans = [s for t in smolagents_session.traces for s in t.spans] + for span in all_spans: + if isinstance(span, InferenceSpan): + assistant_content = span.messages[1].content + text_only = all(hasattr(c, "text") for c in assistant_content) + if text_only: + assert any(c.text for c in assistant_content) diff --git a/tests/strands_evals/mappers/test_utils.py b/tests/strands_evals/mappers/test_utils.py index 730e1d5c..658caab3 100644 --- a/tests/strands_evals/mappers/test_utils.py +++ b/tests/strands_evals/mappers/test_utils.py @@ -204,6 +204,12 @@ def test_skips_spans_without_scope(self): mapper = detect_otel_mapper(spans) assert isinstance(mapper, OpenInferenceSessionMapper) + def test_detects_smolagents_openinference_scope(self): + """Detects OpenInferenceSessionMapper for smolagents openinference scope.""" + spans = [make_span_dict(scope_name="openinference.instrumentation.smolagents")] + mapper = detect_otel_mapper(spans) + assert isinstance(mapper, OpenInferenceSessionMapper) + class TestReadableSpansToDicts: def test_converts_readable_spans(self):