diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/.env.example b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/.env.example
new file mode 100644
index 00000000..d8fa35fb
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/.env.example
@@ -0,0 +1,33 @@
+# =============================================================================
+# .env.example — copy to .env and fill in your values for local development
+# =============================================================================
+
+# ---------------------------------------------------------------------------
+# Azure OpenAI credentials
+# ---------------------------------------------------------------------------
+AZURE_OPENAI_API_KEY=
+AZURE_OPENAI_ENDPOINT=https://<your-resource>.cognitiveservices.azure.com/
+AZURE_OPENAI_DEPLOYMENT=gpt-4o-mini
+AZURE_OPENAI_API_VERSION=2024-12-01-preview
+
+# Alternative: use the OpenAI-compatible endpoint (recommended — reports
+# gen_ai.request.model correctly in telemetry instead of "gpt-4o-mini-...-deployment")
+# OPENAI_BASE_URL=https://<your-resource>.openai.azure.com/openai/deployments/<deployment>/
+# OPENAI_API_KEY=<your-azure-api-key>
+# OPENAI_MODEL=gpt-4o-mini
+
+# ---------------------------------------------------------------------------
+# OpenTelemetry — local development
+# ---------------------------------------------------------------------------
+OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
+OTEL_EXPORTER_OTLP_PROTOCOL=grpc
+OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE=delta
+OTEL_SERVICE_NAME=multi-agent-travel-planner-gunicorn
+OTEL_RESOURCE_ATTRIBUTES=deployment.environment=local
+OTEL_INSTRUMENTATION_GENAI_EMITTERS=span_metric
+OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=SPAN_ONLY
+# Required on Azure App Service (opentelemetry-instrument does not configure
+# the MeterProvider unless these are explicitly set)
+OTEL_METRICS_EXPORTER=otlp
+OTEL_TRACES_EXPORTER=otlp
+OTEL_LOGS_EXPORTER=otlp
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/README.md b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/README.md
new file mode 100644
index 00000000..cd992682
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/README.md
@@ -0,0 +1,322 @@
+# Multi-Agent Travel Planner — Gunicorn + Azure App Service
+
+A LangGraph multi-agent travel planner served by **Gunicorn + Uvicorn workers** with
+OpenTelemetry instrumentation sending `gen_ai.*` spans and metrics to Splunk Observability Cloud.
+
+Five specialized agents collaborate to produce a full itinerary:
+
+```
+coordinator_gc → flight_specialist_gc → hotel_specialist_gc → activity_specialist_gc → plan_synthesizer_gc
+```
+
+---
+
+## Architecture
+
+```
+HTTP client
+    │
+    ▼
+Azure App Service  (Gunicorn + UvicornWorker, FastAPI)
+    │  OTLP/gRPC
+    ▼
+Azure Container Instance  (Splunk OTel Collector 0.123.0)
+    │  signalfx exporter + otlphttp/splunk
+    ▼
+Splunk Observability Cloud  (APM traces + gen_ai.* metrics)
+```
+
+---
+
+## Local development
+
+### Prerequisites
+
+```bash
+pip install splunk-otel-instrumentation-langchain==0.1.14
+pip install -r requirements.txt
+```
+
+Copy `.env.example` to `~/.env` and fill in your Azure OpenAI credentials and OTel settings:
+
+```bash
+cp .env.example ~/.env
+# edit ~/.env
+```
+
+Start a local Splunk OTel Collector (or use `otel-tui` for quick inspection):
+
+```bash
+# Example with Docker — replace <TOKEN> and <REALM>
+docker run -d --name otelcol-local \
+  -p 4317:4317 -p 4318:4318 -p 13133:13133 \
+  -e SPLUNK_ACCESS_TOKEN=<TOKEN> \
+  -e SPLUNK_REALM=<REALM> \
+  quay.io/signalfx/splunk-otel-collector:0.123.0
+```
+
+### Run the app
+
+OTel is initialised **programmatically** inside `app.py` (see
+[OTel troubleshooting — use programmatic auto-instrumentation](https://opentelemetry.io/docs/zero-code/python/troubleshooting/#use-programmatic-auto-instrumentation)).
+Run Gunicorn directly — no `opentelemetry-instrument` wrapper needed:
+
+```bash
+source ~/.env
+
+OTEL_SERVICE_NAME=multi-agent-travel-planner-gunicorn \
+OTEL_RESOURCE_ATTRIBUTES=deployment.environment=local \
+OTEL_INSTRUMENTATION_GENAI_EMITTERS=span_metric \
+OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=SPAN_ONLY \
+OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE=delta \
+OTEL_METRICS_EXPORTER=otlp \
+OTEL_TRACES_EXPORTER=otlp \
+OTEL_LOGS_EXPORTER=otlp \
+gunicorn \
+  -w 1 \
+  -k uvicorn.workers.UvicornWorker \
+  app:app \
+  --access-logfile "-" \
+  --timeout 301 \
+  --bind 0.0.0.0:8000
+```
+
+### Run without OTel (quick test)
+
+```bash
+source ~/.env
+uvicorn app:app --port 8000
+```
+
+### Test requests
+
+```bash
+# Health check
+curl http://localhost:8000/health
+
+# Plan a trip
+curl -X POST http://localhost:8000/plan \
+  -H "Content-Type: application/json" \
+  -d '{"origin":"Seattle","destination":"Tokyo","travellers":2}'
+```
+
+---
+
+## Azure deployment
+
+### Prerequisites
+
+- Azure CLI installed and authenticated (`az login`)
+- Contributor access to an Azure resource group
+- Splunk Observability Cloud access token and realm
+
+### Step 1 — Deploy the OTel Collector to ACI
+
+The collector receives OTLP from the App Service and forwards to Splunk. Deploy it
+first so you have the collector IP for Step 3.
+
+```bash
+export SPLUNK_ACCESS_TOKEN=<your-ingest-token>
+export SPLUNK_HEC_TOKEN=<your-hec-token>
+export SPLUNK_HEC_URL=https://http-inputs-<realm>.splunkcloud.com:443/services/collector/event
+export SPLUNK_REALM=<realm>          # e.g. us1
+export RESOURCE_GROUP=<your-rg>
+export STORAGE_ACCOUNT=<unique-lowercase-name>   # max 24 chars
+
+chmod +x collector/deploy-aci.sh
+./collector/deploy-aci.sh
+```
+
+The script creates an Azure File Share, uploads `otel-collector-config.yaml`, and
+starts the container. Note the **Public IP** printed at the end.
+
+Collector image: `quay.io/signalfx/splunk-otel-collector:0.123.0`
+(see `collector/deploy-aci.sh` — override with `CONTAINER_IMAGE=...` if needed).
+
+### Step 2 — Create the App Service plan and web app
+
+```bash
+export RESOURCE_GROUP=<your-rg>
+export LOCATION=westus
+export PLAN_NAME=<your-plan>
+export APP_NAME=<globally-unique-app-name>
+
+# Create Linux App Service plan (B1 is sufficient)
+az appservice plan create \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${PLAN_NAME}" \
+  --location "${LOCATION}" \
+  --is-linux \
+  --sku B1
+
+# Create the web app (Python 3.12)
+az webapp create \
+  --resource-group "${RESOURCE_GROUP}" \
+  --plan "${PLAN_NAME}" \
+  --name "${APP_NAME}" \
+  --runtime "PYTHON|3.12" \
+  --startup-file "sh startup.sh"
+
+# Enable Oryx build during zip deployment.
+# Without this, az webapp deployment source config-zip extracts the zip but
+# does NOT run pip install, so packages from requirements.txt are missing and
+# startup.sh fails with "gunicorn: not found" or import errors.
+az webapp config appsettings set \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}" \
+  --settings SCM_DO_BUILD_DURING_DEPLOYMENT=true \
+  --output none
+```
+
+### Step 3 — Configure application settings
+
+Replace placeholders with your values. Use the collector IP from Step 1.
+
+```bash
+az webapp config appsettings set \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}" \
+  --settings \
+    AZURE_OPENAI_API_KEY="<your-api-key>" \
+    AZURE_OPENAI_ENDPOINT="https://<your-resource>.cognitiveservices.azure.com/" \
+    AZURE_OPENAI_DEPLOYMENT="gpt-4o-mini" \
+    AZURE_OPENAI_API_VERSION="2024-12-01-preview" \
+    OTEL_EXPORTER_OTLP_ENDPOINT="http://<COLLECTOR_IP>:4317" \
+    OTEL_EXPORTER_OTLP_PROTOCOL="grpc" \
+    OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE="delta" \
+    OTEL_SERVICE_NAME="multi-agent-travel-planner-azure" \
+    OTEL_RESOURCE_ATTRIBUTES="deployment.environment=<your-env>" \
+    OTEL_INSTRUMENTATION_GENAI_EMITTERS="span_metric" \
+    OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT="SPAN_ONLY" \
+    OTEL_METRICS_EXPORTER="otlp" \
+    OTEL_TRACES_EXPORTER="otlp" \
+    OTEL_LOGS_EXPORTER="otlp"
+```
+
+### Step 4 — Build and deploy the app
+
+Run from the `gunicorn/` directory:
+
+```bash
+cd instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn
+
+zip -j /tmp/gunicorn-deploy.zip app.py requirements.txt startup.sh
+
+# config-zip triggers the Oryx build that installs requirements.txt into antenv.
+# (az webapp deploy --type zip skips the Oryx build and leaves packages uninstalled.)
+az webapp deployment source config-zip \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}" \
+  --src /tmp/gunicorn-deploy.zip
+```
+
+### Step 5 — Verify
+
+```bash
+# Health check
+curl https://${APP_NAME}.azurewebsites.net/health
+
+# Plan a trip
+curl -X POST https://${APP_NAME}.azurewebsites.net/plan \
+  -H "Content-Type: application/json" \
+  -d '{"origin":"Seattle","destination":"Paris","travellers":2}'
+
+# Tail live logs
+az webapp log tail \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}"
+
+# Check collector health
+curl http://<COLLECTOR_IP>:13133/
+```
+
+---
+
+## App management
+
+```bash
+# Stop (pauses billing on B1)
+az webapp stop --resource-group "${RESOURCE_GROUP}" --name "${APP_NAME}"
+
+# Start
+az webapp start --resource-group "${RESOURCE_GROUP}" --name "${APP_NAME}"
+
+# Redeploy after code changes
+zip -j /tmp/gunicorn-deploy.zip app.py requirements.txt startup.sh
+az webapp deployment source config-zip \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}" \
+  --src /tmp/gunicorn-deploy.zip
+
+# Update collector IP if ACI was recreated
+az webapp config appsettings set \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}" \
+  --settings OTEL_EXPORTER_OTLP_ENDPOINT="http://<NEW_COLLECTOR_IP>:4317"
+az webapp restart --resource-group "${RESOURCE_GROUP}" --name "${APP_NAME}"
+```
+
+---
+
+## Expected telemetry
+
+In **Splunk APM** (filter by `deployment.environment = <your-env>`):
+
+| Signal | What you see |
+|---|---|
+| Traces | One root trace per `/plan` request; child spans per agent (`coordinator_gc`, `flight_specialist_gc`, …) |
+| Spans | `gen_ai.system`, `gen_ai.request.model`, `gen_ai.operation.name` on each LLM call |
+| Metrics | `gen_ai.client.operation.duration` histogram, `gen_ai.client.token.usage` histogram |
+| Agent view | Per-agent requests, latency, token usage, quality scores |
+
+---
+
+## How OTel instrumentation works
+
+### Programmatic initialization (fork-safe)
+
+OTel is initialized **inside the worker process** via
+[`opentelemetry.instrumentation.auto_instrumentation.initialize()`](https://opentelemetry.io/docs/zero-code/python/troubleshooting/#use-programmatic-auto-instrumentation)
+at the top of `app.py`, guarded so it runs exactly once per process:
+
+```python
+from opentelemetry import trace
+from opentelemetry.sdk.trace import TracerProvider
+
+if not isinstance(trace.get_tracer_provider(), TracerProvider):
+    from opentelemetry.instrumentation.auto_instrumentation import initialize
+    initialize()
+```
+
+**Why not `opentelemetry-instrument gunicorn`?**
+
+The CLI wrapper initializes the OTel SDK in the Gunicorn **master** process. After
+`fork()`, only the calling thread is preserved in each worker — the
+`PeriodicExportingMetricReader` timer thread is silently lost, so **metrics are never
+exported** even though traces continue to flow (the `BatchSpanProcessor` is more
+resilient to fork).
+
+The programmatic approach runs `initialize()` **after** fork, giving each worker its
+own fresh metric reader thread.
+
+Reference: [Pre-fork server issues — OTel Python troubleshooting](https://opentelemetry.io/docs/zero-code/python/troubleshooting/#pre-fork-server-issues)
+
+Support matrix for `opentelemetry-instrument` (multiple workers):
+
+| Stack | Traces | Metrics | Logs |
+|---|---|---|---|
+| Uvicorn | ✓ | ✗ | ✓ |
+| Gunicorn | ✓ | ✗ | ✓ |
+| **Gunicorn + UvicornWorker** | **✓** | **✓** | **✓** |
+
+> On **Linux** (Azure App Service), Gunicorn + UvicornWorker with the CLI wrapper
+> also works because UvicornWorker handles fork safety. On **macOS**, gRPC C extensions
+> cause a SIGSEGV after fork — use the programmatic approach for local development.
+
+### Collector version
+
+The ACI collector is pinned to `quay.io/signalfx/splunk-otel-collector:0.123.0`.
+
+The `sapm` exporter was deprecated in `0.115.0` and **removed in `0.147.0`**. The
+config uses `otlphttp/splunk` for traces, which is compatible with all versions from
+`0.115.0` onwards.
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/app.py b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/app.py
new file mode 100644
index 00000000..1e9b7acb
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/app.py
@@ -0,0 +1,603 @@
+"""Multi-Agent Travel Planner — FastAPI + LangGraph served by Gunicorn + Uvicorn workers.
+
+Overview
+────────
+A five-agent LangGraph pipeline that produces a complete travel itinerary:
+
+  coordinator → flight_specialist → hotel_specialist
+              → activity_specialist → plan_synthesizer
+
+Each agent is an independent LLM call (Azure OpenAI or standard OpenAI) tagged
+with ``gen_ai.agent.name`` metadata so that per-agent telemetry is visible in
+Splunk Observability Cloud's APM Agent view.
+
+Telemetry emitted (via splunk-otel-instrumentation-langchain)
+──────────────────────────────────────────────────────────────
+Spans:
+  - One ``gen_ai.agent.invoke`` span per LangGraph node (coordinator,
+    flight_specialist, hotel_specialist, activity_specialist, plan_synthesizer)
+  - One ``gen_ai.client.chat`` span per LLM call inside each node
+
+Metrics (delta temporality):
+  - ``gen_ai.client.token.usage``   — prompt / completion tokens per agent
+  - ``gen_ai.client.operation.duration`` — latency histogram per operation
+
+All signals carry ``deployment.environment``, ``service.name``,
+``gen_ai.agent.name``, and ``gen_ai.request.model`` attributes.
+
+OTel initialisation strategy
+─────────────────────────────
+Two approaches are supported and can be selected at startup time:
+
+  A) CLI auto-instrumentation (zero-code, recommended for Gunicorn + UvicornWorker):
+
+       opentelemetry-instrument gunicorn -w N -k uvicorn.workers.UvicornWorker app:app
+
+     UvicornWorker is fork-aware and preserves background threads, so all three
+     signals (traces, metrics, logs) flow correctly without extra configuration.
+     Reference: https://opentelemetry.io/docs/zero-code/python/troubleshooting/#pre-fork-server-issues
+
+  B) Programmatic auto-instrumentation (used in this file / Azure App Service):
+
+       gunicorn -w N -k uvicorn.workers.UvicornWorker app:app  # startup.sh
+
+     ``initialize()`` is called at module import time, inside each worker process
+     after the Gunicorn ``fork()``.  This ensures the ``PeriodicExportingMetricReader``
+     background thread starts in the right process, preventing the silent metric
+     drop that occurs when the SDK is initialised in the master process and then
+     inherited across a fork (Linux).
+     Reference: https://opentelemetry.io/docs/zero-code/python/troubleshooting/#use-programmatic-auto-instrumentation
+
+Guard logic (lines below the docstring):
+  1. ``sys.modules`` sentinel — prevents re-running ``initialize()`` if app.py is
+     reimported within the same process (hot-reload, test collection).
+  2. ``TracerProvider`` type check — skips ``initialize()`` when the CLI wrapper
+     (approach A) has already set up a real SDK provider in this process, so both
+     approaches coexist cleanly without double-initialisation.
+
+Support matrix (Gunicorn + UvicornWorker, verified on Azure App Service):
+  Approach        Workers  Traces  Metrics  Logs  Notes
+  CLI wrapper       1        ✓       ✓        ✓
+  CLI wrapper       N        ✓       ✓        ✓   UvicornWorker handles fork safety
+  Programmatic      1        ✓       ✓        ✓
+  Programmatic      N        ✓       ✓        ✓   initialize() runs post-fork per worker
+
+Verified deployments
+─────────────────────
+• Local:         uvicorn app:app --reload  (development)
+• Local:         gunicorn -w 1 -k uvicorn.workers.UvicornWorker app:app
+• Azure App Svc: Python 3.12 runtime, startup.sh, Splunk OTel Collector 0.123.0 in ACI
+  - Traces visible in Splunk APM Trace Analyzer
+  - Metrics (token usage, operation duration) visible in Splunk APM Agent view
+
+Required environment variables
+────────────────────────────────
+  AZURE_OPENAI_ENDPOINT            Azure OpenAI resource endpoint
+  AZURE_OPENAI_API_KEY             Azure OpenAI API key
+  AZURE_OPENAI_DEPLOYMENT          Model deployment name (e.g. gpt-4o-mini)
+  AZURE_OPENAI_API_VERSION         API version (e.g. 2024-02-01)
+
+  OTEL_SERVICE_NAME                Service name shown in Splunk
+  OTEL_RESOURCE_ATTRIBUTES         e.g. deployment.environment=my-env
+  OTEL_EXPORTER_OTLP_ENDPOINT      OTel Collector endpoint (e.g. http://host:4317)
+  OTEL_EXPORTER_OTLP_PROTOCOL      grpc  (recommended)
+  OTEL_METRICS_EXPORTER            otlp
+  OTEL_TRACES_EXPORTER             otlp
+  OTEL_LOGS_EXPORTER               otlp
+  OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE  delta
+  OTEL_INSTRUMENTATION_GENAI_EMITTERS                span_metric
+  OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT SPAN_ONLY
+
+Test:
+    curl -X POST http://localhost:8000/plan \\
+        -H "Content-Type: application/json" \\
+        -d '{"origin":"Seattle","destination":"Tokyo","travellers":2}'
+"""
+
+from __future__ import annotations
+
+import sys
+
+# ---------------------------------------------------------------------------
+# Programmatic OTel initialisation — approach B (see docstring above).
+#
+# Guard logic:
+#   1. sys.modules sentinel: prevents re-running if app.py is re-imported
+#      within the same process (e.g. hot-reload, test collection).
+#   2. SDK provider check: skips initialize() when the CLI wrapper
+#      (opentelemetry-instrument) has already set up a real TracerProvider
+#      in this process.  This lets both approach A and B coexist cleanly:
+#      - approach A: CLI inits first → SDK provider present → skip here
+#      - approach B: no CLI → proxy provider present → initialize() runs here
+# ---------------------------------------------------------------------------
+_OTEL_INIT_KEY = "__travel_planner_otel_initialized__"
+if _OTEL_INIT_KEY not in sys.modules:
+    sys.modules[_OTEL_INIT_KEY] = True  # type: ignore[assignment]
+    try:
+        from opentelemetry import trace as _otel_trace
+        from opentelemetry.sdk.trace import TracerProvider as _SDKTracerProvider
+
+        if not isinstance(_otel_trace.get_tracer_provider(), _SDKTracerProvider):
+            from opentelemetry.instrumentation.auto_instrumentation import initialize
+
+            initialize()
+    except Exception:
+        pass  # SDK not installed — safe to continue without telemetry
+
+import json  # noqa: E402
+import os  # noqa: E402
+import random  # noqa: E402
+from datetime import datetime, timedelta  # noqa: E402
+from pathlib import Path  # noqa: E402
+from typing import Annotated, List, Optional, TypedDict  # noqa: E402
+from uuid import uuid4  # noqa: E402
+
+from dotenv import load_dotenv  # noqa: E402
+
+# Load ~/.env so AzureOpenAI credentials and OTEL vars are available at import time.
+load_dotenv(Path.home() / ".env")
+
+from fastapi import FastAPI, HTTPException  # noqa: E402
+from langchain.agents import create_agent as _create_react_agent  # noqa: E402
+from langchain_core.messages import (  # noqa: E402
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.tools import tool  # noqa: E402
+from langchain_openai import AzureChatOpenAI, ChatOpenAI  # noqa: E402
+from langgraph.graph import END, START, StateGraph  # noqa: E402
+from langgraph.graph.message import AnyMessage, add_messages  # noqa: E402
+from pydantic import BaseModel  # noqa: E402
+
+# ---------------------------------------------------------------------------
+# LLM factory — auto-detects AzureOpenAI or standard OpenAI from env vars.
+# Agent names have "gc" suffix to distinguish this gunicorn deployment.
+# ---------------------------------------------------------------------------
+
+_GC_SUFFIX = "_gc"
+
+
+def _create_llm(
+    agent_name: str, *, temperature: float, session_id: str
+) -> ChatOpenAI | AzureChatOpenAI:
+    """Create an LLM tagged with the gc-suffixed agent name."""
+    model = os.environ.get("OPENAI_MODEL", "gpt-4o-mini")
+    full_name = f"{agent_name}{_GC_SUFFIX}"
+    tags = [f"agent:{full_name}", "travel-planner-gc"]
+    metadata = {
+        "agent_name": full_name,
+        "agent_type": full_name,
+        "session_id": session_id,
+        "thread_id": session_id,
+        "ls_model_name": model,
+        "ls_temperature": temperature,
+    }
+
+    # Prefer OpenAI-compatible Azure endpoint (OPENAI_BASE_URL) — reports
+    # gen_ai.request.model correctly in telemetry.
+    base_url = os.environ.get("OPENAI_BASE_URL")
+    if base_url:
+        return ChatOpenAI(
+            model=model,
+            api_key=os.environ.get("OPENAI_API_KEY", ""),
+            base_url=base_url,
+            temperature=temperature,
+            tags=tags,
+            metadata=metadata,
+        )
+
+    if os.environ.get("AZURE_OPENAI_ENDPOINT"):
+        # AZURE_OPENAI_DEPLOYMENT is the canonical name used in Azure App Service docs;
+        # AZURE_CHAT_DEPLOYMENT is the legacy name kept for backward compatibility.
+        deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT") or os.environ.get(
+            "AZURE_CHAT_DEPLOYMENT", model
+        )
+        return AzureChatOpenAI(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            api_key=os.environ.get("AZURE_OPENAI_API_KEY", ""),
+            api_version=os.environ.get("AZURE_OPENAI_API_VERSION", "2024-02-01"),
+            azure_deployment=deployment,
+            temperature=temperature,
+            tags=tags,
+            model_kwargs={"metadata": metadata},
+        )
+
+    return ChatOpenAI(
+        model=model,
+        temperature=temperature,
+        tags=tags,
+        metadata=metadata,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Sample data
+# ---------------------------------------------------------------------------
+
+DESTINATIONS = {
+    "paris": {
+        "country": "France",
+        "currency": "EUR",
+        "airport": "CDG",
+        "highlights": [
+            "Eiffel Tower at sunset",
+            "Seine dinner cruise",
+            "Day trip to Versailles",
+        ],
+    },
+    "tokyo": {
+        "country": "Japan",
+        "currency": "JPY",
+        "airport": "HND",
+        "highlights": [
+            "Tsukiji market food tour",
+            "Ghibli Museum visit",
+            "Day trip to Hakone hot springs",
+        ],
+    },
+    "rome": {
+        "country": "Italy",
+        "currency": "EUR",
+        "airport": "FCO",
+        "highlights": [
+            "Colosseum underground tour",
+            "Private pasta masterclass",
+            "Sunset walk through Trastevere",
+        ],
+    },
+}
+
+
+def _compute_dates() -> tuple[str, str]:
+    start = datetime.now() + timedelta(days=30)
+    end = start + timedelta(days=7)
+    return start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")
+
+
+# ---------------------------------------------------------------------------
+# Tools
+# ---------------------------------------------------------------------------
+
+
+@tool
+def mock_search_flights(origin: str, destination: str, departure: str) -> str:
+    """Return mock flight options for a given origin/destination pair."""
+    random.seed(hash((origin, destination, departure)) % (2**32))
+    airline = random.choice(["SkyLine", "AeroJet", "CloudNine"])
+    fare = random.randint(700, 1250)
+    return (
+        f"Top choice: {airline} non-stop {origin}->{destination}, "
+        f"depart {departure} 09:15, arrive 17:05. Premium economy ${fare} return."
+    )
+
+
+@tool
+def mock_search_hotels(destination: str, check_in: str, check_out: str) -> str:
+    """Return mock hotel recommendation for the stay."""
+    random.seed(hash((destination, check_in, check_out)) % (2**32))
+    name = random.choice(["Grand Meridian", "Hotel Lumière", "The Atlas"])
+    rate = random.randint(240, 410)
+    return (
+        f"{name} near the historic centre. Boutique suites, rooftop bar, "
+        f"average nightly rate ${rate} including breakfast."
+    )
+
+
+@tool
+def mock_search_activities(destination: str) -> str:
+    """Return a short list of signature activities for the destination."""
+    data = DESTINATIONS.get(destination.lower(), DESTINATIONS["paris"])
+    bullets = "\n".join(f"- {item}" for item in data["highlights"])
+    return f"Signature experiences in {destination.title()}:\n{bullets}"
+
+
+# ---------------------------------------------------------------------------
+# LangGraph state
+# ---------------------------------------------------------------------------
+
+
+class PlannerState(TypedDict):
+    messages: Annotated[List[AnyMessage], add_messages]
+    user_request: str
+    session_id: str
+    origin: str
+    destination: str
+    departure: str
+    return_date: str
+    travellers: int
+    flight_summary: Optional[str]
+    hotel_summary: Optional[str]
+    activities_summary: Optional[str]
+    final_itinerary: Optional[str]
+    current_agent: str
+
+
+# ---------------------------------------------------------------------------
+# LangGraph nodes — agent names use _gc suffix
+# ---------------------------------------------------------------------------
+
+
+def coordinator_node(state: PlannerState) -> PlannerState:
+    llm = _create_llm("coordinator", temperature=0.2, session_id=state["session_id"])
+    agent = _create_react_agent(llm, tools=[]).with_config(
+        {
+            "run_name": f"coordinator{_GC_SUFFIX}",
+            "tags": ["agent", f"agent:coordinator{_GC_SUFFIX}"],
+            "metadata": {
+                "agent_name": f"coordinator{_GC_SUFFIX}",
+                "session_id": state["session_id"],
+            },
+        }
+    )
+    system_message = SystemMessage(
+        content=(
+            "You are the lead travel coordinator. Extract the key details from the "
+            "traveller's request and describe the plan for the specialist agents."
+        )
+    )
+    result = agent.invoke({"messages": [system_message] + list(state["messages"])})
+    final_message = result["messages"][-1]
+    state["messages"].append(
+        final_message
+        if isinstance(final_message, BaseMessage)
+        else AIMessage(content=str(final_message))
+    )
+    state["current_agent"] = "flight_specialist"
+    return state
+
+
+def flight_specialist_node(state: PlannerState) -> PlannerState:
+    llm = _create_llm(
+        "flight_specialist", temperature=0.4, session_id=state["session_id"]
+    )
+    agent = _create_react_agent(llm, tools=[mock_search_flights]).with_config(
+        {
+            "run_name": f"flight_specialist{_GC_SUFFIX}",
+            "tags": ["agent", f"agent:flight_specialist{_GC_SUFFIX}"],
+            "metadata": {
+                "agent_name": f"flight_specialist{_GC_SUFFIX}",
+                "session_id": state["session_id"],
+            },
+        }
+    )
+    step = (
+        f"Find an appealing flight from {state['origin']} to {state['destination']} "
+        f"departing {state['departure']} for {state['travellers']} travellers."
+    )
+    result = agent.invoke({"messages": [HumanMessage(content=step)]})
+    final_message = result["messages"][-1]
+    state["flight_summary"] = (
+        final_message.content
+        if isinstance(final_message, BaseMessage)
+        else str(final_message)
+    )
+    state["messages"].append(
+        final_message
+        if isinstance(final_message, BaseMessage)
+        else AIMessage(content=str(final_message))
+    )
+    state["current_agent"] = "hotel_specialist"
+    return state
+
+
+def hotel_specialist_node(state: PlannerState) -> PlannerState:
+    llm = _create_llm(
+        "hotel_specialist", temperature=0.5, session_id=state["session_id"]
+    )
+    agent = _create_react_agent(llm, tools=[mock_search_hotels]).with_config(
+        {
+            "run_name": f"hotel_specialist{_GC_SUFFIX}",
+            "tags": ["agent", f"agent:hotel_specialist{_GC_SUFFIX}"],
+            "metadata": {
+                "agent_name": f"hotel_specialist{_GC_SUFFIX}",
+                "session_id": state["session_id"],
+            },
+        }
+    )
+    step = (
+        f"Recommend a boutique hotel in {state['destination']} between "
+        f"{state['departure']} and {state['return_date']} for {state['travellers']} travellers."
+    )
+    result = agent.invoke({"messages": [HumanMessage(content=step)]})
+    final_message = result["messages"][-1]
+    state["hotel_summary"] = (
+        final_message.content
+        if isinstance(final_message, BaseMessage)
+        else str(final_message)
+    )
+    state["messages"].append(
+        final_message
+        if isinstance(final_message, BaseMessage)
+        else AIMessage(content=str(final_message))
+    )
+    state["current_agent"] = "activity_specialist"
+    return state
+
+
+def activity_specialist_node(state: PlannerState) -> PlannerState:
+    llm = _create_llm(
+        "activity_specialist", temperature=0.6, session_id=state["session_id"]
+    )
+    agent = _create_react_agent(llm, tools=[mock_search_activities]).with_config(
+        {
+            "run_name": f"activity_specialist{_GC_SUFFIX}",
+            "tags": ["agent", f"agent:activity_specialist{_GC_SUFFIX}"],
+            "metadata": {
+                "agent_name": f"activity_specialist{_GC_SUFFIX}",
+                "session_id": state["session_id"],
+            },
+        }
+    )
+    step = f"Curate signature activities for travellers spending a week in {state['destination']}."
+    result = agent.invoke({"messages": [HumanMessage(content=step)]})
+    final_message = result["messages"][-1]
+    state["activities_summary"] = (
+        final_message.content
+        if isinstance(final_message, BaseMessage)
+        else str(final_message)
+    )
+    state["messages"].append(
+        final_message
+        if isinstance(final_message, BaseMessage)
+        else AIMessage(content=str(final_message))
+    )
+    state["current_agent"] = "plan_synthesizer"
+    return state
+
+
+def plan_synthesizer_node(state: PlannerState) -> PlannerState:
+    llm = _create_llm(
+        "plan_synthesizer", temperature=0.3, session_id=state["session_id"]
+    )
+    system_prompt = SystemMessage(
+        content=(
+            "You are the travel plan synthesiser. Combine the specialist insights into a "
+            "concise, structured itinerary covering flights, accommodation and activities."
+        )
+    )
+    content = json.dumps(
+        {
+            "flight": state["flight_summary"],
+            "hotel": state["hotel_summary"],
+            "activities": state["activities_summary"],
+        },
+        indent=2,
+    )
+    response = llm.invoke(
+        [
+            system_prompt,
+            HumanMessage(
+                content=(
+                    f"Traveller request: {state['user_request']}\n\n"
+                    f"Origin: {state['origin']} | Destination: {state['destination']}\n"
+                    f"Dates: {state['departure']} to {state['return_date']}\n\n"
+                    f"Specialist summaries:\n{content}"
+                )
+            ),
+        ]
+    )
+    state["final_itinerary"] = response.content
+    state["messages"].append(response)
+    state["current_agent"] = "completed"
+    return state
+
+
+def should_continue(state: PlannerState) -> str:
+    mapping = {
+        "start": "coordinator",
+        "flight_specialist": "flight_specialist",
+        "hotel_specialist": "hotel_specialist",
+        "activity_specialist": "activity_specialist",
+        "plan_synthesizer": "plan_synthesizer",
+    }
+    return mapping.get(state["current_agent"], END)
+
+
+def build_workflow() -> StateGraph:
+    graph = StateGraph(PlannerState)
+    graph.add_node("coordinator", coordinator_node)
+    graph.add_node("flight_specialist", flight_specialist_node)
+    graph.add_node("hotel_specialist", hotel_specialist_node)
+    graph.add_node("activity_specialist", activity_specialist_node)
+    graph.add_node("plan_synthesizer", plan_synthesizer_node)
+    graph.add_conditional_edges(START, should_continue)
+    graph.add_conditional_edges("coordinator", should_continue)
+    graph.add_conditional_edges("flight_specialist", should_continue)
+    graph.add_conditional_edges("hotel_specialist", should_continue)
+    graph.add_conditional_edges("activity_specialist", should_continue)
+    graph.add_conditional_edges("plan_synthesizer", should_continue)
+    return graph
+
+
+# ---------------------------------------------------------------------------
+# FastAPI app
+# ---------------------------------------------------------------------------
+
+app = FastAPI(
+    title="Multi-Agent Travel Planner (Gunicorn)",
+    description="LangGraph travel planner served by Gunicorn + Uvicorn workers with zero-code OTel.",
+    version="0.1.0",
+)
+
+
+class PlanRequest(BaseModel):
+    origin: str = "Seattle"
+    destination: str = "Paris"
+    travellers: int = 2
+    user_request: Optional[str] = None
+
+
+class PlanResponse(BaseModel):
+    session_id: str
+    origin: str
+    destination: str
+    departure: str
+    return_date: str
+    travellers: int
+    flight_summary: Optional[str]
+    hotel_summary: Optional[str]
+    activities_summary: Optional[str]
+    final_itinerary: Optional[str]
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}
+
+
+@app.post("/plan", response_model=PlanResponse)
+async def plan(request: PlanRequest):
+    """Run the multi-agent travel planner and return the itinerary."""
+    session_id = str(uuid4())
+    departure, return_date = _compute_dates()
+
+    user_request = request.user_request or (
+        f"Planning a week-long trip from {request.origin} to {request.destination}. "
+        "Looking for a boutique hotel, comfortable flights and unique local experiences."
+    )
+
+    initial_state: PlannerState = {
+        "messages": [HumanMessage(content=user_request)],
+        "user_request": user_request,
+        "session_id": session_id,
+        "origin": request.origin,
+        "destination": request.destination,
+        "departure": departure,
+        "return_date": return_date,
+        "travellers": request.travellers,
+        "flight_summary": None,
+        "hotel_summary": None,
+        "activities_summary": None,
+        "final_itinerary": None,
+        "current_agent": "start",
+    }
+
+    workflow = build_workflow()
+    compiled = workflow.compile()
+    config = {"configurable": {"thread_id": session_id}, "recursion_limit": 10}
+
+    final_state: Optional[PlannerState] = None
+    try:
+        for step in compiled.stream(initial_state, config):
+            _, node_state = next(iter(step.items()))
+            final_state = node_state
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc)) from exc
+
+    if not final_state:
+        raise HTTPException(status_code=500, detail="Workflow produced no state")
+
+    return PlanResponse(
+        session_id=session_id,
+        origin=request.origin,
+        destination=request.destination,
+        departure=departure,
+        return_date=return_date,
+        travellers=request.travellers,
+        flight_summary=final_state.get("flight_summary"),
+        hotel_summary=final_state.get("hotel_summary"),
+        activities_summary=final_state.get("activities_summary"),
+        final_itinerary=final_state.get("final_itinerary"),
+    )
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/README.md b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/README.md
new file mode 100644
index 00000000..32ac24f1
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/README.md
@@ -0,0 +1,94 @@
+# Splunk OTel Collector — Azure Container Instance
+
+This directory contains the configuration and deployment script for running the Splunk Distribution of the OpenTelemetry Collector as an **Azure Container Instance (ACI)** gateway.
+
+The collector receives traces and metrics from the App Service web app over OTLP gRPC (port 4317) and forwards them to Splunk Observability Cloud.
+
+## Prerequisites
+
+- Azure CLI installed and logged in (`az login`)
+- Contributor rights on the target resource group
+- Splunk Observability Cloud ingest token and realm
+
+## Required environment variables
+
+| Variable | Description | Example |
+|---|---|---|
+| `SPLUNK_ACCESS_TOKEN` | Splunk Observability Cloud ingest token | `gXgmP9v-...` |
+| `SPLUNK_HEC_TOKEN` | Splunk HEC token for log ingestion | `bdef2e63-...` |
+| `SPLUNK_HEC_URL` | Splunk HEC endpoint URL | `https://http-inputs-<realm>.splunkcloud.com:443/services/collector/event` |
+| `SPLUNK_REALM` | Splunk Observability Cloud realm | `us1` |
+| `RESOURCE_GROUP` | Azure resource group | `my-resource-group` |
+| `STORAGE_ACCOUNT` | Storage account name (globally unique, lowercase, max 24 chars) | `myotelcfgstorage` |
+
+## Optional overrides
+
+| Variable | Default | Description |
+|---|---|---|
+| `LOCATION` | `westus` | Azure region (must match the resource group) |
+| `CONTAINER_NAME` | `splunk-otel-collector` | ACI container name |
+| `DEPLOYMENT_ENV` | `azure` | Value for `deployment.environment` resource attribute |
+| `SPLUNK_MEMORY_LIMIT_MIB` | `900` | Memory ceiling for the collector's `memory_limiter` processor |
+
+## Deploy
+
+```bash
+export SPLUNK_ACCESS_TOKEN=<your-ingest-token>
+export SPLUNK_HEC_TOKEN=<your-hec-token>
+export SPLUNK_HEC_URL=https://http-inputs-<realm>.splunkcloud.com:443/services/collector/event
+export SPLUNK_REALM=us1
+export RESOURCE_GROUP=<your-resource-group>
+export STORAGE_ACCOUNT=<your-storage-account>
+
+chmod +x collector/deploy-aci.sh
+./collector/deploy-aci.sh
+```
+
+The script prints the container's public IP at the end. Use it to configure `OTEL_EXPORTER_OTLP_ENDPOINT` in the App Service settings:
+
+```bash
+az webapp config appsettings set \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name "${APP_NAME}" \
+  --settings OTEL_EXPORTER_OTLP_ENDPOINT="http://<COLLECTOR_IP>:4317"
+```
+
+## Operations
+
+```bash
+# Health check
+curl http://<COLLECTOR_IP>:13133/
+
+# Tail live logs
+az container logs \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name splunk-otel-collector \
+  --follow
+
+# Restart
+az container restart \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name splunk-otel-collector
+
+# Delete
+az container delete \
+  --resource-group "${RESOURCE_GROUP}" \
+  --name splunk-otel-collector \
+  --yes
+```
+
+## Architecture
+
+```
+App Service (Gunicorn + Uvicorn)
+        │ OTLP gRPC :4317
+        ▼
+Azure Container Instance
+  splunk-otel-collector
+        │ sapm
+        ├──────────────► Splunk APM (traces)
+        │ signalfx
+        ├──────────────► Splunk IMM (metrics)
+        │ splunk_hec
+        └──────────────► Splunk Log Observer (logs)
+```
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/deploy-aci.sh b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/deploy-aci.sh
new file mode 100644
index 00000000..a879dd51
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/deploy-aci.sh
@@ -0,0 +1,180 @@
+#!/usr/bin/env bash
+# =============================================================================
+# deploy-aci.sh — Deploy Splunk OTel Collector to Azure Container Instance
+#
+# Prerequisites:
+#   - Azure CLI installed and logged in (az login)
+#   - Contributor rights on the resource group
+#
+# Required environment variables (set before running):
+#   SPLUNK_ACCESS_TOKEN   Splunk Observability Cloud ingest token
+#   SPLUNK_HEC_TOKEN      Splunk HEC token for log ingestion
+#   SPLUNK_HEC_URL        Splunk HEC endpoint URL
+#   SPLUNK_REALM          Splunk realm (e.g. us1, eu0)
+#   RESOURCE_GROUP        Azure resource group
+#   STORAGE_ACCOUNT       Storage account name (globally unique, lowercase, max 24 chars)
+#
+# Optional overrides (defaults shown below):
+#   LOCATION              Azure region (default: westus)
+#   CONTAINER_NAME        ACI container name (default: splunk-otel-collector)
+#   DEPLOYMENT_ENV        deployment.environment tag (default: azure)
+#   SPLUNK_MEMORY_LIMIT_MIB  Memory ceiling in MiB (default: 900)
+#
+# Usage:
+#   export SPLUNK_ACCESS_TOKEN=<your-ingest-token>
+#   export SPLUNK_HEC_TOKEN=<your-hec-token>
+#   export SPLUNK_HEC_URL=https://http-inputs-<realm>.splunkcloud.com:443/services/collector/event
+#   export SPLUNK_REALM=us1
+#   export RESOURCE_GROUP=<your-resource-group>
+#   export STORAGE_ACCOUNT=<your-storage-account>
+#   chmod +x deploy-aci.sh && ./deploy-aci.sh
+# =============================================================================
+set -euo pipefail
+
+# ---------------------------------------------------------------------------
+# Validate required env vars
+# ---------------------------------------------------------------------------
+: "${SPLUNK_ACCESS_TOKEN:?Required env var SPLUNK_ACCESS_TOKEN is not set}"
+: "${SPLUNK_HEC_TOKEN:?Required env var SPLUNK_HEC_TOKEN is not set}"
+: "${SPLUNK_HEC_URL:?Required env var SPLUNK_HEC_URL is not set}"
+: "${SPLUNK_REALM:?Required env var SPLUNK_REALM is not set}"
+: "${RESOURCE_GROUP:?Required env var RESOURCE_GROUP is not set}"
+: "${STORAGE_ACCOUNT:?Required env var STORAGE_ACCOUNT is not set (globally unique, lowercase, max 24 chars)}"
+
+# ---------------------------------------------------------------------------
+# Config — override via env vars or edit defaults here
+# ---------------------------------------------------------------------------
+LOCATION="${LOCATION:-westus}"   # must match your RG location
+FILE_SHARE="${FILE_SHARE:-otelconfig}"
+CONFIG_FILE="$(dirname "$0")/otel-collector-config.yaml"
+
+CONTAINER_NAME="${CONTAINER_NAME:-splunk-otel-collector}"
+# Pinned to 0.123.0 — the sapm exporter was deprecated in 0.115.0 and removed
+# in 0.147.0 (collector v1.12.0). If upgrading past 0.147.0, verify the
+# otel-collector-config.yaml uses otlphttp/splunk instead of sapm for traces.
+CONTAINER_IMAGE="${CONTAINER_IMAGE:-quay.io/signalfx/splunk-otel-collector:0.123.0}"
+
+DEPLOYMENT_ENV="${DEPLOYMENT_ENV:-azure}"
+
+# Derived from realm
+SPLUNK_API_URL="https://api.${SPLUNK_REALM}.signalfx.com"
+SPLUNK_INGEST_URL="https://ingest.${SPLUNK_REALM}.signalfx.com"
+SPLUNK_TRACE_URL="https://ingest.${SPLUNK_REALM}.signalfx.com/v2/trace"
+
+# Container paths for the Splunk OTel Collector image (fixed for this image)
+SPLUNK_BUNDLE_DIR="/usr/lib/splunk-otel-collector/agent-bundle"
+SPLUNK_COLLECTD_DIR="/usr/lib/splunk-otel-collector/agent-bundle/run/collectd"
+SPLUNK_LISTEN_INTERFACE="0.0.0.0"
+SPLUNK_MEMORY_LIMIT_MIB="${SPLUNK_MEMORY_LIMIT_MIB:-900}"
+
+# ---------------------------------------------------------------------------
+# 1. Create Storage Account + File Share (for the config file)
+# ---------------------------------------------------------------------------
+echo ""
+echo "==> [1/4] Creating storage account: ${STORAGE_ACCOUNT}"
+az storage account create \
+    --name "${STORAGE_ACCOUNT}" \
+    --resource-group "${RESOURCE_GROUP}" \
+    --location "${LOCATION}" \
+    --sku Standard_LRS \
+    --kind StorageV2 \
+    --output none
+
+STORAGE_KEY=$(az storage account keys list \
+    --resource-group "${RESOURCE_GROUP}" \
+    --account-name "${STORAGE_ACCOUNT}" \
+    --query "[0].value" \
+    --output tsv)
+
+echo "==> [1/4] Creating file share: ${FILE_SHARE}"
+az storage share create \
+    --name "${FILE_SHARE}" \
+    --account-name "${STORAGE_ACCOUNT}" \
+    --account-key "${STORAGE_KEY}" \
+    --output none
+
+# ---------------------------------------------------------------------------
+# 2. Upload collector config to the file share
+# ---------------------------------------------------------------------------
+echo ""
+echo "==> [2/4] Uploading otel-collector-config.yaml to file share"
+az storage file upload \
+    --share-name "${FILE_SHARE}" \
+    --source "${CONFIG_FILE}" \
+    --path "otel-collector-config.yaml" \
+    --account-name "${STORAGE_ACCOUNT}" \
+    --account-key "${STORAGE_KEY}" \
+    --output none
+
+# ---------------------------------------------------------------------------
+# 3. Deploy the Azure Container Instance
+# ---------------------------------------------------------------------------
+echo ""
+echo "==> [3/4] Creating container instance: ${CONTAINER_NAME}"
+
+az container create \
+    --resource-group "${RESOURCE_GROUP}" \
+    --name "${CONTAINER_NAME}" \
+    --image "${CONTAINER_IMAGE}" \
+    --os-type Linux \
+    --cpu 1 \
+    --memory 2 \
+    --restart-policy Always \
+    \
+    --ports 4317 4318 13133 6060 9411 \
+    --ip-address Public \
+    \
+    --azure-file-volume-account-name "${STORAGE_ACCOUNT}" \
+    --azure-file-volume-account-key "${STORAGE_KEY}" \
+    --azure-file-volume-share-name "${FILE_SHARE}" \
+    --azure-file-volume-mount-path /etc/otel \
+    \
+    --command-line "/otelcol --config /etc/otel/otel-collector-config.yaml" \
+    \
+    --environment-variables \
+        SPLUNK_ACCESS_TOKEN="${SPLUNK_ACCESS_TOKEN}" \
+        SPLUNK_HEC_TOKEN="${SPLUNK_HEC_TOKEN}" \
+        SPLUNK_HEC_URL="${SPLUNK_HEC_URL}" \
+        SPLUNK_API_URL="${SPLUNK_API_URL}" \
+        SPLUNK_INGEST_URL="${SPLUNK_INGEST_URL}" \
+        SPLUNK_TRACE_URL="${SPLUNK_TRACE_URL}" \
+        SPLUNK_REALM="${SPLUNK_REALM}" \
+        SPLUNK_BUNDLE_DIR="${SPLUNK_BUNDLE_DIR}" \
+        SPLUNK_COLLECTD_DIR="${SPLUNK_COLLECTD_DIR}" \
+        SPLUNK_LISTEN_INTERFACE="${SPLUNK_LISTEN_INTERFACE}" \
+        SPLUNK_MEMORY_LIMIT_MIB="${SPLUNK_MEMORY_LIMIT_MIB}" \
+        OTEL_RESOURCE_ATTRIBUTES="deployment.environment=${DEPLOYMENT_ENV}" \
+    \
+    --output table
+
+# ---------------------------------------------------------------------------
+# 4. Show the public IP so we can update OTEL_EXPORTER_OTLP_ENDPOINT
+# ---------------------------------------------------------------------------
+echo ""
+echo "==> [4/4] Fetching container IP address"
+COLLECTOR_IP=$(az container show \
+    --resource-group "${RESOURCE_GROUP}" \
+    --name "${CONTAINER_NAME}" \
+    --query "ipAddress.ip" \
+    --output tsv)
+
+echo ""
+echo "============================================================"
+echo " Splunk OTel Collector deployed successfully!"
+echo "============================================================"
+echo " Container: ${CONTAINER_NAME}"
+echo " Public IP: ${COLLECTOR_IP}"
+echo ""
+echo " Update your App Service configuration:"
+echo ""
+echo "   az webapp config appsettings set \\"
+echo "     --resource-group \"${RESOURCE_GROUP}\" \\"
+echo "     --name \"\${APP_NAME}\" \\"
+echo "     --settings OTEL_EXPORTER_OTLP_ENDPOINT=\"http://${COLLECTOR_IP}:4317\""
+echo ""
+echo " Health check:"
+echo "   curl http://${COLLECTOR_IP}:13133/"
+echo ""
+echo " View logs:"
+echo "   az container logs --resource-group ${RESOURCE_GROUP} --name ${CONTAINER_NAME} --follow"
+echo "============================================================"
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/otel-collector-config.yaml b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/otel-collector-config.yaml
new file mode 100644
index 00000000..74f50bd6
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/collector/otel-collector-config.yaml
@@ -0,0 +1,142 @@
+# Splunk OTel Collector — gateway config for Azure Container Instance
+#
+# Tested with: quay.io/signalfx/splunk-otel-collector:0.123.0
+#
+# Required env vars (set in ACI via deploy-aci.sh):
+#   SPLUNK_ACCESS_TOKEN   Splunk Observability Cloud ingest token
+#   SPLUNK_API_URL        e.g. https://api.us1.signalfx.com
+#   SPLUNK_INGEST_URL     e.g. https://ingest.us1.signalfx.com
+#   SPLUNK_TRACE_URL      e.g. https://ingest.us1.signalfx.com/v2/trace
+#   SPLUNK_HEC_TOKEN      Splunk HEC token for log ingestion
+#   SPLUNK_HEC_URL        e.g. https://http-inputs-<realm>.splunkcloud.com:443/services/collector/event
+#   SPLUNK_BUNDLE_DIR     /usr/lib/splunk-otel-collector/agent-bundle
+#   SPLUNK_COLLECTD_DIR   /usr/lib/splunk-otel-collector/agent-bundle/run/collectd
+#   SPLUNK_LISTEN_INTERFACE  0.0.0.0 in ACI
+#   SPLUNK_MEMORY_LIMIT_MIB  e.g. 900
+#
+# Note: deployment.environment is set by the application via OTEL_RESOURCE_ATTRIBUTES.
+# The resourcedetection processor uses override: false so it never overwrites
+# app-supplied attributes.
+
+extensions:
+  health_check:
+    endpoint: "${SPLUNK_LISTEN_INTERFACE}:13133"
+  http_forwarder:
+    ingress:
+      endpoint: "${SPLUNK_LISTEN_INTERFACE}:6060"
+    egress:
+      endpoint: "${SPLUNK_API_URL}"
+
+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: "${SPLUNK_LISTEN_INTERFACE}:4317"
+      http:
+        endpoint: "${SPLUNK_LISTEN_INTERFACE}:4318"
+  jaeger:
+    protocols:
+      grpc:
+        endpoint: "${SPLUNK_LISTEN_INTERFACE}:14250"
+      thrift_binary:
+        endpoint: "${SPLUNK_LISTEN_INTERFACE}:6832"
+      thrift_compact:
+        endpoint: "${SPLUNK_LISTEN_INTERFACE}:6831"
+      thrift_http:
+        endpoint: "${SPLUNK_LISTEN_INTERFACE}:14268"
+  zipkin:
+    endpoint: "${SPLUNK_LISTEN_INTERFACE}:9411"
+  signalfx:
+    endpoint: "${SPLUNK_LISTEN_INTERFACE}:9943"
+  prometheus/internal:
+    config:
+      scrape_configs:
+        - job_name: 'otel-collector'
+          scrape_interval: 10s
+          static_configs:
+            - targets: ["localhost:8888"]
+          metric_relabel_configs:
+            - source_labels: [__name__]
+              regex: 'otelcol_rpc_.*'
+              action: drop
+            - source_labels: [__name__]
+              regex: 'otelcol_http_.*'
+              action: drop
+            - source_labels: [__name__]
+              regex: 'otelcol_processor_batch_.*'
+              action: drop
+  nop:
+
+processors:
+  batch:
+    metadata_keys:
+      - X-SF-Token
+  memory_limiter:
+    check_interval: 2s
+    limit_mib: ${SPLUNK_MEMORY_LIMIT_MIB}
+  resourcedetection:
+    detectors: [azure, system]
+    # override: false preserves app-sent attributes (e.g. deployment.environment
+    # from OTEL_RESOURCE_ATTRIBUTES). Cloud/host attributes from the detector are
+    # still added when they are absent from the incoming resource.
+    override: false
+exporters:
+  # Traces → Splunk APM via OTLP HTTP (replaces sapm, removed in latest image)
+  otlphttp/splunk:
+    traces_endpoint: "${SPLUNK_INGEST_URL}/v2/trace/otlp"
+    headers:
+      X-SF-Token: "${SPLUNK_ACCESS_TOKEN}"
+  signalfx:
+    access_token: "${SPLUNK_ACCESS_TOKEN}"
+    api_url: "${SPLUNK_API_URL}"
+    ingest_url: "${SPLUNK_INGEST_URL}"
+    sync_host_metadata: true
+    correlation:
+    send_otlp_histograms: true
+  otlphttp/entities:
+    logs_endpoint: "${SPLUNK_INGEST_URL}/v3/event"
+    headers:
+      "X-SF-Token": "${SPLUNK_ACCESS_TOKEN}"
+  splunk_hec:
+    token: "${SPLUNK_HEC_TOKEN}"
+    endpoint: "${SPLUNK_HEC_URL}"
+    source: "otel"
+    sourcetype: "otel"
+    profiling_data_enabled: false
+  splunk_hec/profiling:
+    token: "${SPLUNK_ACCESS_TOKEN}"
+    endpoint: "${SPLUNK_INGEST_URL}/v1/log"
+    log_data_enabled: false
+  debug:
+    verbosity: basic
+
+service:
+  telemetry:
+    metrics:
+      level: basic
+  extensions: [health_check, http_forwarder]
+  pipelines:
+    traces:
+      receivers: [jaeger, otlp, zipkin]
+      processors: [memory_limiter, batch, resourcedetection]
+      exporters: [otlphttp/splunk, signalfx]
+    metrics:
+      receivers: [otlp, signalfx]
+      processors: [memory_limiter, batch, resourcedetection]
+      exporters: [signalfx]
+    metrics/internal:
+      receivers: [prometheus/internal]
+      processors: [memory_limiter, batch, resourcedetection]
+      exporters: [signalfx]
+    logs/signalfx:
+      receivers: [signalfx]
+      processors: [memory_limiter, batch, resourcedetection]
+      exporters: [signalfx]
+    logs/entities:
+      receivers: [nop]
+      processors: [memory_limiter, batch, resourcedetection]
+      exporters: [otlphttp/entities]
+    logs:
+      receivers: [otlp]
+      processors: [memory_limiter, batch, resourcedetection]
+      exporters: [splunk_hec, splunk_hec/profiling]
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/requirements.txt b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/requirements.txt
new file mode 100644
index 00000000..8a7cc5f9
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/requirements.txt
@@ -0,0 +1,11 @@
+fastapi
+gunicorn
+uvicorn[standard]
+langchain
+langchain-core
+langchain-openai
+langgraph
+python-dotenv
+opentelemetry-distro
+opentelemetry-exporter-otlp
+splunk-otel-instrumentation-langchain==0.1.14
diff --git a/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/startup.sh b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/startup.sh
new file mode 100644
index 00000000..fc901a28
--- /dev/null
+++ b/instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/gunicorn/startup.sh
@@ -0,0 +1,59 @@
+#!/bin/sh
+# =============================================================================
+# startup.sh — Azure App Service entry point for FastAPI + Gunicorn + OTel
+#
+# Why this file exists:
+# App Service runs startup commands with /bin/sh (not bash).
+# Oryx builds a virtual environment called "antenv" and sets PYTHONPATH to
+# its site-packages, but does NOT add antenv/bin to PATH.
+# This means `opentelemetry-instrument` and `gunicorn` are not found without
+# first activating the virtual environment.
+#
+# This script:
+# 1. Activates antenv using POSIX-compatible `.` (not bash `source`)
+# 2. Runs opentelemetry-instrument gunicorn with Uvicorn workers
+#
+# Startup command to set in App Service → Configuration → General settings:
+#   sh startup.sh
+# =============================================================================
+
+set -e
+
+echo "[startup] Python: $(python3 --version 2>&1)"
+echo "[startup] Working directory: $(pwd)"
+
+# Activate the Oryx-built virtual environment.
+# antenv is always co-located with app.py after Oryx extracts the zip.
+if [ -f "antenv/bin/activate" ]; then
+    # shellcheck disable=SC1091
+    . antenv/bin/activate
+    echo "[startup] Activated antenv at $(pwd)/antenv"
+else
+    echo "[startup] WARNING: antenv/bin/activate not found — falling back to system Python"
+    echo "[startup] Contents of current directory:"
+    ls -la
+fi
+
+# App Service sets PORT; fall back to 8000 (App Service default for custom apps).
+APP_PORT="${PORT:-8000}"
+echo "[startup] Launching on port ${APP_PORT}"
+echo "[startup] Service: ${OTEL_SERVICE_NAME:-multi-agent-travel-planner-azure}"
+echo "[startup] OTLP endpoint: ${OTEL_EXPORTER_OTLP_ENDPOINT:-not set}"
+echo "[startup] Environment: ${OTEL_RESOURCE_ATTRIBUTES:-not set}"
+
+# OTel is initialised programmatically inside app.py via initialize() with a
+# sys.modules guard (see the top of app.py for the full explanation).
+# This runs post-fork in each Gunicorn worker, giving every worker its own
+# fresh PeriodicExportingMetricReader thread — fixing the silent metric drop
+# caused by the opentelemetry-instrument wrapper + --preload pattern.
+#
+# UvicornWorker: required to serve the FastAPI ASGI application.
+# --timeout 301: slightly above the default 300s to allow the LLM pipeline
+# to complete before Gunicorn kills a slow worker.
+exec gunicorn \
+    -w 1 \
+    -k uvicorn.workers.UvicornWorker \
+    app:app \
+    --access-logfile "-" \
+    --timeout 301 \
+    --bind "0.0.0.0:${APP_PORT}"