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Add LangChain invoke workflow and invoke agent span support (#25)
* Add LangChain invoke workflow and invoke agent span support * Added change log * fixed errors * updated changelog * Addressed lzchen comments * updated uv * addressed * fixed tests instrumentation-langchain-conformance * fixed tests * fixed test * fixed tests * Generate updated CI workflows for langchain instrumentation Add missing py312/py313 test jobs for instrumentation-langchain. Assisted-by: Claude Opus 4.6 --------- Co-authored-by: Mike Goldsmith <goldsmith.mike@gmail.com>
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‎.github/workflows/test.yml‎

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

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

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Add LangChain workflow and agent span support

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

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

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

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langchain==0.3.21
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langchain_openai
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langgraph
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opentelemetry-sdk>=1.42.0
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opentelemetry-exporter-otlp-proto-grpc>=1.42.0

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

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

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