Langchain examples for testing

and send with otlp and full chatbot demo

---

So... turns out Langchain uses the v1beta1 prediction service client
under the hood directly.. So we should probably instrument that after
all instead of the main wrapper API.

It also has a streaming option so we should try to support that as well,
and it has ainvoke() for asyncio.

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/.dockerignore

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+.venv
+docker-compose.yaml

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/.python-version

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+3.13

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/README.md

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+This sample contains part of the LangGraph chatbot demo taken from
+https://python.langchain.com/docs/tutorials/chatbot, running with OTel instrumentation. It
+sends traces and logs to the OTel collector which sends them to GCP. Docker compose wraps
+everything to make it easy to run.
+
+## Running the example
+
+I recommend running in Cloud Shell, it's super simple. You will see GenAI spans in trace
+explorer right away. Make sure the Vertex and Trace APIs are enabled in the project.
+
+### Cloud Shell or GCE
+
+```sh
+git clone --branch=vertex-langgraph https://github.com/aabmass/opentelemetry-python-contrib.git
+cd opentelemetry-python-contrib/instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo
+docker compose up --build --abort-on-container-exit
+```
+
+### Locally with Application Default Credentials
+
+```sh
+git clone --branch=vertex-langgraph https://github.com/aabmass/opentelemetry-python-contrib.git
+cd opentelemetry-python-contrib/instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo
+
+# Export the credentials to `GOOGLE_APPLICATION_CREDENTIALS` environment variable so it is
+# available inside the docker containers
+export GOOGLE_APPLICATION_CREDENTIALS=$HOME/.config/gcloud/application_default_credentials.json
+# Lets collector read mounted config
+export USERID="$(id -u)"
+# Specify the project ID
+export GOOGLE_CLOUD_PROJECT=<your project id>
+docker compose up --build --abort-on-container-exit
+```

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/chatbot.py

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+# https://python.langchain.com/docs/tutorials/chatbot
+
+from os import environ
+from typing import Sequence
+
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    trim_messages,
+)
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_google_vertexai import ChatVertexAI
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import add_messages
+from typing_extensions import Annotated, TypedDict
+
+from opentelemetry import trace
+
+
+def main() -> None:
+    model = ChatVertexAI(
+        model="gemini-1.5-flash",
+        project=environ.get("GOOGLE_CLOUD_PROJECT", None),
+    )
+
+    prompt_template = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a helpful assistant. Answer all questions to the best of your ability in {language}.",
+            ),
+            MessagesPlaceholder(variable_name="messages"),
+        ]
+    )
+
+    class State(TypedDict):
+        messages: Annotated[Sequence[BaseMessage], add_messages]
+        language: str
+
+    trimmer = trim_messages(
+        max_tokens=200,
+        strategy="last",
+        token_counter=model,
+        include_system=True,
+        allow_partial=False,
+        start_on="human",
+    )
+
+    messages = [
+        SystemMessage(content="you're a good assistant"),
+        HumanMessage(content="hi! I'm bob"),
+        AIMessage(content="hi!"),
+        HumanMessage(content="I like vanilla ice cream"),
+        AIMessage(content="nice"),
+        HumanMessage(content="whats 2 + 2"),
+        AIMessage(content="4"),
+        HumanMessage(content="thanks"),
+        AIMessage(content="no problem!"),
+        HumanMessage(content="having fun?"),
+        AIMessage(content="yes!"),
+    ]
+
+    workflow = StateGraph(state_schema=State)
+
+    def call_model(state: State):
+        trimmed_messages = trimmer.invoke(state["messages"])
+        prompt = prompt_template.invoke(
+            {"messages": trimmed_messages, "language": state["language"]}
+        )
+        response = model.invoke(prompt)
+        return {"messages": [response]}
+
+    workflow.add_edge(START, "model")
+    workflow.add_node("model", call_model)
+
+    memory = MemorySaver()
+    app = workflow.compile(checkpointer=memory)
+
+    config = {"configurable": {"thread_id": "abc567"}}
+    query = "What is my name?"
+    language = "English"
+
+    input_messages = messages + [HumanMessage(query)]
+    output = app.invoke(
+        {"messages": input_messages, "language": language},
+        config,
+    )
+    output["messages"][-1].pretty_print()
+
+    config = {"configurable": {"thread_id": "abc678"}}
+    query = "What math problem did I ask?"
+    language = "English"
+
+    input_messages = messages + [HumanMessage(query)]
+    output = app.invoke(
+        {"messages": input_messages, "language": language},
+        config,
+    )
+    output["messages"][-1].pretty_print()
+
+
+with trace.get_tracer(__name__).start_as_current_span("demo-root-span"):
+    main()

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/docker-compose.yaml

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+services:
+  app:
+    build:
+      dockerfile_inline: |
+        FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim
+        RUN apt-get update && apt-get install -y git
+
+        WORKDIR app/
+        COPY pyproject.toml uv.lock /app
+        RUN uv sync --frozen --no-dev
+        ENV PATH="/app/.venv/bin:$PATH"
+        COPY . /app
+        ENTRYPOINT []
+        CMD ["opentelemetry-instrument", "python", "chatbot.py"]
+    volumes:
+      - ${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:ro
+    environment:
+      - OTEL_EXPORTER_OTLP_ENDPOINT=http://otelcol:4317
+      - OTEL_SERVICE_NAME=langgraph-chatbot-demo
+      - OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true
+      - OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true
+
+      - GOOGLE_CLOUD_PROJECT
+      - GOOGLE_CLOUD_QUOTA_PROJECT
+      - GOOGLE_APPLICATION_CREDENTIALS
+    depends_on:
+      - otelcol
+
+  otelcol:
+    image: otel/opentelemetry-collector-contrib:0.118.0
+    volumes:
+      - ./otel-collector-config.yaml:/etc/otelcol-contrib/config.yaml:ro
+      - ${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:${GOOGLE_APPLICATION_CREDENTIALS:-/dev/null}:ro
+    environment:
+      - GOOGLE_CLOUD_PROJECT
+      - GOOGLE_CLOUD_QUOTA_PROJECT
+      - GOOGLE_APPLICATION_CREDENTIALS
+    # If the collector does not have permission to read the mounted volumes, set
+    # USERID=$(id -u) to run the container as the current user
+    user: $USERID
+
+volumes:
+  logs:

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/otel-collector-config.yaml

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+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: "0.0.0.0:4317"
+
+extensions:
+  googleclientauth:
+    project: ${GOOGLE_CLOUD_PROJECT}
+    quota_project: ${GOOGLE_CLOUD_QUOTA_PROJECT}
+    scopes:
+      - "https://www.googleapis.com/auth/trace.append"
+      - "https://www.googleapis.com/auth/cloud-platform"
+
+processors:
+  resource:
+    attributes:
+      - key: gcp.project_id
+        value: ${GOOGLE_CLOUD_PROJECT}
+        action: insert
+
+exporters:
+  googlecloud:
+    project: ${GOOGLE_CLOUD_PROJECT}
+    log:
+      default_log_name: "collector-otlp-logs"
+  otlp:
+    endpoint: https://telemetry.us-central1.rep.googleapis.com:443
+    auth:
+      authenticator: googleclientauth
+
+service:
+  extensions: [googleclientauth]
+  pipelines:
+    traces:
+      receivers: [otlp]
+      processors: [resource]
+      exporters: [otlp]
+    logs:
+      receivers: [otlp]
+      processors: [resource]
+      exporters: [googlecloud]

instrumentation-genai/opentelemetry-instrumentation-vertexai/examples/langgraph-chatbot-demo/pyproject.toml

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+[project]
+name = "langgraph-chatbot-demo"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.9"
+dependencies = [
+    "langchain-core>=0.3.31",
+    "langchain-google-vertexai>=2.0.7",
+    "langgraph>0.2.27",
+    "opentelemetry-distro>=0.50b0",
+    "opentelemetry-exporter-otlp-proto-grpc>=1.29.0",
+    "opentelemetry-instrumentation-vertexai",
+]
+
+[tool.uv.sources]
+opentelemetry-instrumentation-vertexai = { git = "https://github.com/aabmass/opentelemetry-python-contrib.git", subdirectory = "instrumentation-genai/opentelemetry-instrumentation-vertexai", branch = "vertex-langgraph" }
+
+[dependency-groups]
+dev = [
+    "ruff>=0.9.2",
+]