Add LangChain invoke workflow and invoke agent span support

instrumentation-genai/opentelemetry-instrumentation-langchain/CHANGELOG.md

@@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+- Add LangChain workflow and agent span support. Also refactor LLM invocation.
+  ([#4449](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/4449))
 - Fix compatibility with wrapt 2.x by using positional arguments in `wrap_function_wrapper()` calls
   ([#4445](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/4445))
 - Added span support for genAI langchain llm invocation.

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/agent/main.py

@@ -0,0 +1,104 @@
+# Copyright The OpenTelemetry Authors
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+ReAct agent example built with LangGraph.
+
+Uses LangGraph's prebuilt create_react_agent with simple calculator tools.
+OpenTelemetry LangChain instrumentation traces the LLM calls made by the agent.
+"""
+
+from uuid import uuid4
+
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+
+from opentelemetry import _logs, metrics, trace
+from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
+    OTLPLogExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
+    OTLPMetricExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+    OTLPSpanExporter,
+)
+from opentelemetry.instrumentation.langchain import LangChainInstrumentor
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+
+# Configure tracing
+trace.set_tracer_provider(TracerProvider())
+span_processor = BatchSpanProcessor(OTLPSpanExporter())
+trace.get_tracer_provider().add_span_processor(span_processor)
+
+# Configure logging
+_logs.set_logger_provider(LoggerProvider())
+_logs.get_logger_provider().add_log_record_processor(
+    BatchLogRecordProcessor(OTLPLogExporter())
+)
+
+# Configure metrics
+metrics.set_meter_provider(
+    MeterProvider(
+        metric_readers=[
+            PeriodicExportingMetricReader(
+                OTLPMetricExporter(),
+            ),
+        ]
+    )
+)
+
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers together."""
+    return a * b
+
+
+@tool
+def add(a: float, b: float) -> float:
+    """Add two numbers together."""
+    return a + b
+
+
+def main():
+    LangChainInstrumentor().instrument()
+
+    llm = ChatOpenAI(
+        model="gpt-3.5-turbo",
+        temperature=0.1,
+        max_tokens=100,
+        top_p=0.9,
+        seed=100,
+    )
+
+    session_id = str(uuid4())
+    agent = create_react_agent(llm, tools=[multiply, add]).with_config(
+        {
+            "metadata": {
+                "agent_name": "coordinator",
+                "session_id": session_id,
+            },
+        }
+    )
+
+    result = agent.invoke(
+        {"messages": [HumanMessage(content="What is (3 * 4) + 7?")]}
+    )
+
+    print("Agent output:")
+    for msg in result["messages"]:
+        print(f"  {type(msg).__name__}: {msg.content}")
+
+    LangChainInstrumentor().uninstrument()
+
+
+if __name__ == "__main__":
+    main()

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/agent/requirements.txt

@@ -0,0 +1,5 @@
+langchain==0.3.21
+langchain_openai
+langgraph
+opentelemetry-sdk>=1.39.0
+opentelemetry-exporter-otlp-proto-grpc>=1.39.0

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/agent/single_node_agent.py

@@ -0,0 +1,122 @@
+# Copyright The OpenTelemetry Authors
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Single-node agent example built with StateGraph.
+
+A single ReAct agent answers arithmetic questions using calculator tools.
+OpenTelemetry LangChain instrumentation traces all LLM calls.
+"""
+
+from uuid import uuid4
+
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, MessagesState, StateGraph
+
+from opentelemetry import _logs, metrics, trace
+from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
+    OTLPLogExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
+    OTLPMetricExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+    OTLPSpanExporter,
+)
+from opentelemetry.instrumentation.langchain import LangChainInstrumentor
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+
+# Configure tracing
+trace.set_tracer_provider(TracerProvider())
+trace.get_tracer_provider().add_span_processor(
+    BatchSpanProcessor(OTLPSpanExporter())
+)
+
+# Configure logging
+_logs.set_logger_provider(LoggerProvider())
+_logs.get_logger_provider().add_log_record_processor(
+    BatchLogRecordProcessor(OTLPLogExporter())
+)
+
+# Configure metrics
+metrics.set_meter_provider(
+    MeterProvider(
+        metric_readers=[PeriodicExportingMetricReader(OTLPMetricExporter())]
+    )
+)
+
+
+# --- Tools ----------------------------------------------------------------
+
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers."""
+    return a * b
+
+
+@tool
+def add(a: float, b: float) -> float:
+    """Add two numbers."""
+    return a + b
+
+
+# --- Graph ----------------------------------------------------------------
+
+
+def build_single_node_graph(llm: ChatOpenAI):
+    session_id = str(uuid4())
+
+    agent = create_agent(
+        llm, tools=[multiply, add], name="math_agent"
+    ).with_config(
+        {
+            "metadata": {
+                "agent_name": "math_agent",
+                "session_id": session_id,
+            },
+        }
+    )
+
+    def run_agent(state: MessagesState) -> dict:
+        result = agent.invoke({"messages": state["messages"]})
+        return {"messages": result["messages"]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("math_agent", run_agent)
+    builder.add_edge(START, "math_agent")
+    builder.add_edge("math_agent", END)
+
+    return builder.compile()
+
+
+def main():
+    LangChainInstrumentor().instrument()
+
+    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.1, seed=100)
+    graph = build_single_node_graph(llm)
+
+    questions = [
+        "What is 12 multiplied by 7?",
+        "What is 15 plus 27?",
+    ]
+
+    for question in questions:
+        print(f"\nQuestion: {question}")
+        result = graph.invoke({"messages": [HumanMessage(content=question)]})
+        last = result["messages"][-1]
+        print(f"  Answer: {last.content}")
+
+    LangChainInstrumentor().uninstrument()
+
+
+if __name__ == "__main__":
+    main()

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/workflow/main.py

@@ -0,0 +1,144 @@
+# Copyright The OpenTelemetry Authors
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+LangGraph StateGraph example with two LLM nodes.
+
+Graph topology:
+
+  START → researcher → summariser → END
+
+Steps:
+  1. *researcher*  – gathers factual background on the user's question.
+  2. *summariser*  – condenses the researcher's output into a concise answer.
+
+OpenTelemetry LangChain instrumentation traces both LLM calls.
+"""
+
+from typing import Annotated
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from typing_extensions import TypedDict
+
+from opentelemetry import _logs, metrics, trace
+from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
+    OTLPLogExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
+    OTLPMetricExporter,
+)
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+    OTLPSpanExporter,
+)
+from opentelemetry.instrumentation.langchain import LangChainInstrumentor
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+
+# Configure tracing
+trace.set_tracer_provider(TracerProvider())
+trace.get_tracer_provider().add_span_processor(
+    BatchSpanProcessor(OTLPSpanExporter())
+)
+
+# Configure logging
+_logs.set_logger_provider(LoggerProvider())
+_logs.get_logger_provider().add_log_record_processor(
+    BatchLogRecordProcessor(OTLPLogExporter())
+)
+
+# Configure metrics
+metrics.set_meter_provider(
+    MeterProvider(
+        metric_readers=[PeriodicExportingMetricReader(OTLPMetricExporter())]
+    )
+)
+
+
+class GraphState(TypedDict):
+    """State shared across all graph nodes."""
+
+    messages: Annotated[list, add_messages]
+    research: str
+
+
+def build_graph(llm: ChatOpenAI):
+    """Build a StateGraph with a researcher node and a summariser node."""
+
+    def researcher(state: GraphState) -> dict:
+        """Gather factual background on the last user message."""
+        response = llm.invoke(
+            [
+                SystemMessage(
+                    content="You are a research assistant. Provide 2-3 factual sentences."
+                ),
+                HumanMessage(content=state["messages"][-1].content),
+            ]
+        )
+        return {
+            "research": response.content,
+            "messages": [response],
+        }
+
+    def summariser(state: GraphState) -> dict:
+        """Condense the researcher's output into one concise sentence."""
+        response = llm.invoke(
+            [
+                SystemMessage(
+                    content="You are an expert summariser. Condense the text below into one clear sentence."
+                ),
+                HumanMessage(content=state["research"]),
+            ]
+        )
+        return {"messages": [response]}
+
+    builder = StateGraph(GraphState)
+    builder.add_node("researcher", researcher)
+    builder.add_node("summariser", summariser)
+
+    builder.add_edge(START, "researcher")
+    builder.add_edge("researcher", "summariser")
+    builder.add_edge("summariser", END)
+
+    return builder.compile()
+
+
+def main():
+    LangChainInstrumentor().instrument()
+
+    llm = ChatOpenAI(
+        model="gpt-3.5-turbo",
+        temperature=0.1,
+        max_tokens=200,
+        seed=42,
+    )
+
+    graph = build_graph(llm)
+
+    question = "What is the capital of France?"
+    print(f"Question: {question}\n")
+
+    result = graph.invoke(
+        {
+            "messages": [HumanMessage(content=question)],
+            "research": "",
+        }
+    )
+
+    print("Research output:")
+    print(f"  {result['research']}\n")
+
+    print("Final summary:")
+    print(f"  {result['messages'][-1].content}")
+
+    LangChainInstrumentor().uninstrument()
+
+
+if __name__ == "__main__":
+    main()

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/workflow/requirements.txt

@@ -0,0 +1,5 @@
+langchain==0.3.21
+langchain_openai
+langgraph
+opentelemetry-sdk>=1.39.0
+opentelemetry-exporter-otlp-proto-grpc>=1.39.0