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LangGraph console agent with instrumentation sample (#404)
* LangGraph console based instrumentation sample This sample has region tags to go in the public docs. It is a CLI based LangGraph agent that is manually instrumented for Cloud Obs. * Add README for LangGraph sample * Small readme tweaks
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‎samples/langgraph-sql-agent/.python-version

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3.13

‎samples/langgraph-sql-agent/README.md

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# OpenTelemetry LangGraph instrumentation example
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<!-- TODO: link to devsite doc once it is published -->
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This sample is a LangGraph agent instrumented with OpenTelemetry to send traces and logs with
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GenAI prompts and responses, and metrics to Google Cloud Observability.
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The Agent is a SQL expert that has full access to an ephemeral SQLite database. The database is
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initially empty. It is built with the the LangGraph prebuilt [ReAct
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Agent](https://langchain-ai.github.io/langgraph/agents/agents/#basic-configuration#code) and the
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[SQLDatabaseToolkit](https://python.langchain.com/docs/integrations/tools/sql_database/).
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## APIs and Permissions
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Enable the relevant Cloud Observability APIs if they aren't already enabled.
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```sh
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gcloud services enable telemetry.googleapis.com logging.googleapis.com monitoring.googleapis.com cloudtrace.googleapis.com
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```
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This sample writes to Cloud Logging, Cloud Monitoring, and Cloud Trace. Grant yourself the
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following roles to run the example:
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- `roles/logging.logWriter` – see https://cloud.google.com/logging/docs/access-control#permissions_and_roles
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- `roles/monitoring.metricWriter` – see https://cloud.google.com/monitoring/access-control#predefined_roles
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- `roles/telemetry.writer` – see https://cloud.google.com/trace/docs/iam#telemetry-roles
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## Running the example
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The sample can easily be run in Cloud Shell. You can also use
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[Application Default Credentials][ADC] locally. Clone and set environment variables:
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```sh
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git clone https://github.com/GoogleCloudPlatform/opentelemetry-operations-python.git
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cd opentelemetry-operations-python/samples/langgraph-sql-agent
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# Capture GenAI prompts and responses
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export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true
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# Capture application logs automatically
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export OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true
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```
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Create a virtual environment and run the sample:
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```sh
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python -m venv venv/
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source venv/bin/activate
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pip install -r requirements.txt
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python main.py
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```
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Alternatively if you have [`uv`](https://docs.astral.sh/uv/) installed:
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```sh
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uv run main.py
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```
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## Viewing the results
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To view the generated traces with [Generative AI
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events](https://cloud.google.com/trace/docs/finding-traces#view_generative_ai_events) in the
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GCP console, use the [Trace Explorer](https://cloud.google.com/trace/docs/finding-traces). Filter for spans named `invoke agent`.
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[ADC]: https://cloud.google.com/docs/authentication/application-default-credentials

‎samples/langgraph-sql-agent/agent.py

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sqlite3
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import tempfile
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from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
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from langchain_community.utilities.sql_database import SQLDatabase
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.runnables.config import (
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RunnableConfig,
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)
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from langgraph.checkpoint.memory import InMemorySaver
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from langgraph.prebuilt import create_react_agent
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from opentelemetry import trace
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from sqlalchemy import create_engine
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from patched_vertexai import PatchedChatVertexAI
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from utils import ask_prompt, console, print_markdown, render_messages
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SYSTEM_PROMPT = SystemMessage(
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content=f"""\
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You are a helpful AI assistant with a mastery of database design and querying. You have access
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to an ephemeral sqlite3 database that you can query and modify through some tools. Help answer
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questions and perform actions. Follow these rules:
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- Make sure you always use sql_db_query_checker to validate SQL statements **before** running
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them. In pseudocode: `checked_query = sql_db_query_checker(query);
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sql_db_query(checked_query)`.
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- Be creative and don't ask for permission! The database is ephemeral so it's OK to make some mistakes.
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- The sqlite version is {sqlite3.sqlite_version} which supports multiple row inserts.
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- Always prefer to insert multiple rows in a single call to the sql_db_query tool, if possible.
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- You may request to execute multiple sql_db_query tool calls which will be run in parallel.
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If you make a mistake, try to recover."""
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)
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INTRO_TEXT = """\
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Starting agent using ephemeral SQLite DB {dbpath}. This demo allows you to chat with an Agent
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that has full access to an ephemeral SQLite database. The database is initially empty. It is
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built with the the LangGraph prebuilt **ReAct Agent** and the **SQLDatabaseToolkit**. Here are some samples you can try:
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**Weather**
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- Create a new table to hold weather data.
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- Populate the weather database with 20 example rows.
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- Add a new column for weather observer notes
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**Pets**
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- Create a database table for pets including an `owner_id` column.
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- Add 20 example rows please.
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- Create an owner table.
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- Link the two tables together, adding new columns, values, and rows as needed.
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- Write a query to join these tables and give the result of owners and their pets.
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- Show me the query, then the output as a table
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---
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"""
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tracer = trace.get_tracer(__name__)
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def run_agent(*, model_name: str, recursion_limit: int = 50) -> None:
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model = PatchedChatVertexAI(model=model_name)
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checkpointer = InMemorySaver()
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# Ephemeral sqlite database per run
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_, dbpath = tempfile.mkstemp(suffix=".db")
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engine = create_engine(
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f"sqlite:///{dbpath}",
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isolation_level="AUTOCOMMIT",
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)
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# The agent has access to the SQL database through these tools
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db = SQLDatabase(engine)
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toolkit = SQLDatabaseToolkit(db=db, llm=model)
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# Filter out sql_db_list_tables since it only lists the initial tables
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tools = [tool for tool in toolkit.get_tools() if tool.name != "sql_db_list_tables"]
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# Use the prebuilt ReAct agent graph
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# https://langchain-ai.github.io/langgraph/agents/agents/
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agent = create_react_agent(
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model, tools, checkpointer=checkpointer, prompt=SYSTEM_PROMPT
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)
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config: RunnableConfig = {
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"configurable": {"thread_id": "default"},
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"recursion_limit": recursion_limit,
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}
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print_markdown(INTRO_TEXT.format(dbpath=dbpath))
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while True:
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# Accept input from the user
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try:
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prompt_txt = ask_prompt()
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except (EOFError, KeyboardInterrupt):
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print_markdown("Exiting...")
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break
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if not prompt_txt:
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continue
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prompt = HumanMessage(prompt_txt)
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with console.status("Agent is thinking"):
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# [START opentelemetry_langgraph_agent_span]
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# Invoke the agent within a span
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with tracer.start_as_current_span("invoke agent"):
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result = agent.invoke({"messages": [prompt]}, config=config)
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# [END opentelemetry_langgraph_agent_span]
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# Print history
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render_messages(result["messages"])

‎samples/langgraph-sql-agent/main.py

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import google.auth
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import google.auth.transport.requests
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import grpc
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from google.auth.transport.grpc import AuthMetadataPlugin
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from opentelemetry import _events as events
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from opentelemetry import _logs as logs
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from opentelemetry import metrics, trace
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from opentelemetry.exporter.cloud_logging import CloudLoggingExporter
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from opentelemetry.exporter.cloud_monitoring import CloudMonitoringMetricsExporter
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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.sqlite3 import SQLite3Instrumentor
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from opentelemetry.instrumentation.vertexai import VertexAIInstrumentor
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from opentelemetry.sdk._events import EventLoggerProvider
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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.resources import SERVICE_NAME, Resource
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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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from agent import run_agent
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# [START opentelemetry_langgraph_otel_setup]
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def setup_opentelemetry() -> None:
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credentials, project_id = google.auth.default()
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resource = Resource.create(
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attributes={
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SERVICE_NAME: "langgraph-sql-agent",
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# The project to send spans to
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"gcp.project_id": project_id,
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}
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)
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# Set up OTLP auth
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request = google.auth.transport.requests.Request()
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auth_metadata_plugin = AuthMetadataPlugin(credentials=credentials, request=request)
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channel_creds = grpc.composite_channel_credentials(
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grpc.ssl_channel_credentials(),
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grpc.metadata_call_credentials(auth_metadata_plugin),
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)
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# Set up OpenTelemetry Python SDK
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tracer_provider = TracerProvider(resource=resource)
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tracer_provider.add_span_processor(
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BatchSpanProcessor(
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OTLPSpanExporter(
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credentials=channel_creds,
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endpoint="https://telemetry.googleapis.com:443/v1/traces",
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)
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)
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)
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trace.set_tracer_provider(tracer_provider)
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logger_provider = LoggerProvider(resource=resource)
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logger_provider.add_log_record_processor(
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BatchLogRecordProcessor(CloudLoggingExporter())
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)
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logs.set_logger_provider(logger_provider)
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event_logger_provider = EventLoggerProvider(logger_provider)
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events.set_event_logger_provider(event_logger_provider)
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reader = PeriodicExportingMetricReader(CloudMonitoringMetricsExporter())
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meter_provider = MeterProvider(metric_readers=[reader], resource=resource)
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metrics.set_meter_provider(meter_provider)
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# Load instrumentors
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SQLite3Instrumentor().instrument()
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VertexAIInstrumentor().instrument()
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# [END opentelemetry_langgraph_otel_setup]
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# Make sure to set:
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# OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true
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# OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true
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# in order to full prompts and responses and logs messages.
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setup_opentelemetry()
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run_agent(
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model_name="gemini-2.0-flash",
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# You can increase this, but it may incur more token usage
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# https://langchain-ai.github.io/langgraph/troubleshooting/errors/GRAPH_RECURSION_LIMIT/#troubleshooting
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recursion_limit=50,
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)

‎samples/langgraph-sql-agent/patched_vertexai.py

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import Any
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from google.cloud.aiplatform_v1.types import (
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GenerateContentRequest as v1GenerateContentRequest,
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)
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from google.cloud.aiplatform_v1beta1.types import (
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GenerateContentRequest,
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)
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from langchain_core.messages import (
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BaseMessage,
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)
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from langchain_google_vertexai import ChatVertexAI
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class PatchedChatVertexAI(ChatVertexAI):
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def _prepare_request_gemini(
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self, messages: list[BaseMessage], *args: Any, **kwargs: Any
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) -> v1GenerateContentRequest | GenerateContentRequest:
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# See https://github.com/langchain-ai/langchain-google/issues/886
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#
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# Filter out any blocked messages with no content which can appear if you have a blocked
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# message from finish_reason SAFETY:
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#
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# AIMessage(
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# content="",
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# additional_kwargs={},
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# response_metadata={
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# "is_blocked": True,
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# "safety_ratings": [ ... ],
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# "finish_reason": "SAFETY",
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# },
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# ...
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# )
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#
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# These cause `google.api_core.exceptions.InvalidArgument: 400 Unable to submit request
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# because it must include at least one parts field`
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messages = [
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message
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for message in messages
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if not message.response_metadata.get("is_blocked", False)
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]
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return super()._prepare_request_gemini(messages, *args, **kwargs)

‎samples/langgraph-sql-agent/pyproject.toml

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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[project]
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name = "langgraph-sql-agent"
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version = "0.1.0"
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description = "A LangGraph ReAct agent that can run queries on an ephemeral SQLite database"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"langchain-community>=0.3.16",
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"langchain-google-vertexai>=2.0.7",
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"langgraph>=0.4.3",
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"opentelemetry-exporter-gcp-logging>=1.9.0a0",
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"opentelemetry-exporter-gcp-monitoring>=1.9.0a0",
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"opentelemetry-exporter-otlp-proto-grpc>=1.33.0",
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"opentelemetry-instrumentation-httpx>=0.54b0",
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"opentelemetry-instrumentation-requests>=0.54b0",
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"opentelemetry-instrumentation-sqlite3>=0.54b0",
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"opentelemetry-instrumentation-vertexai>=2.0b0",
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"rich>=14.0.0",
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]

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