Add LangChain invoke workflow and invoke agent span support

.github/workflows/test.yml

@@ -1114,6 +1114,44 @@ jobs:
       - name: Run tests
         run: uvx --with tox-uv tox -e py313-test-instrumentation-claude-agent-sdk-latest -- -ra
 
+  py312-test-instrumentation-langchain_ubuntu-latest:
+    name: instrumentation-langchain 3.12 Ubuntu
+    runs-on: ubuntu-latest
+    timeout-minutes: 30
+    steps:
+      - name: Checkout repo @ SHA - ${{ github.sha }}
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+        with:
+          python-version: "3.12"
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0
+
+      - name: Run tests
+        run: uvx --with tox-uv tox -e py312-test-instrumentation-langchain -- -ra
+
+  py313-test-instrumentation-langchain_ubuntu-latest:
+    name: instrumentation-langchain 3.13 Ubuntu
+    runs-on: ubuntu-latest
+    timeout-minutes: 30
+    steps:
+      - name: Checkout repo @ SHA - ${{ github.sha }}
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+
+      - name: Set up Python 3.13
+        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+        with:
+          python-version: "3.13"
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0
+
+      - name: Run tests
+        run: uvx --with tox-uv tox -e py313-test-instrumentation-langchain -- -ra
+
   py312-test-instrumentation-langchain-conformance_ubuntu-latest:
     name: instrumentation-langchain-conformance 3.12 Ubuntu
     runs-on: ubuntu-latest

instrumentation/opentelemetry-instrumentation-langchain/.changelog/25.added

@@ -0,0 +1 @@
+Add LangChain workflow and agent span support

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

@@ -0,0 +1,5 @@
+langchain==0.3.21
+langchain_openai
+langgraph
+opentelemetry-sdk>=1.42.0
+opentelemetry-exporter-otlp-proto-grpc>=1.42.0

instrumentation/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_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, MessagesState, StateGraph
+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())
+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_react_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()