diff --git a/examples/uncommonroute/README.md b/examples/uncommonroute/README.md
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+# AgentOps + UncommonRoute: Track and Optimize LLM Costs
+
+[AgentOps](https://github.com/AgentOps-AI/agentops) tracks what your agents spend. [UncommonRoute](https://github.com/CommonstackAI/UncommonRoute) cuts that spend by routing each request to the cheapest model that can handle it.
+
+Together they close the loop: **observe → optimize → verify**.
+
+## How it works
+
+```
+Your Agent → UncommonRoute (local proxy) → cheapest capable model
+     ↓
+AgentOps (traces every call, tracks cost per request)
+```
+
+UncommonRoute analyzes each request using three signals (metadata, embeddings, structural complexity) and routes it to the optimal model:
+
+| Task complexity | Routed to | Example |
+|----------------|-----------|---------|
+| Trivial | nano model (~$0.0008) | "hello" |
+| Simple | budget model (~$0.001) | "fix the typo on line 3" |
+| Medium | mid-tier (~$0.03) | "refactor this function" |
+| Complex | premium (~$0.05) | "design a distributed scheduler" |
+
+**Result**: ~82% cost reduction with 93.4% task completion rate.
+
+## Setup
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Initialize UncommonRoute (one-time setup)
+uncommon-route init
+
+# Start the local proxy
+uncommon-route serve
+
+# Set your AgentOps API key
+export AGENTOPS_API_KEY="***"
+
+# Run the example
+python uncommonroute_cost_optimization.py
+```
+
+## What you'll see in the AgentOps dashboard
+
+- Cost breakdown per LLM call
+- Which model UncommonRoute selected for each task
+- Total session cost (compare with always-premium routing)
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diff --git a/examples/uncommonroute/requirements.txt b/examples/uncommonroute/requirements.txt
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+agentops
+openai
+python-dotenv
+uncommon-route
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diff --git a/examples/uncommonroute/uncommonroute_cost_optimization.py b/examples/uncommonroute/uncommonroute_cost_optimization.py
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+"""
+AgentOps + UncommonRoute: Track and Optimize LLM Costs
+
+This example shows how to combine AgentOps (cost tracking) with
+UncommonRoute (cost optimization) to get full visibility into your
+LLM spending and automatically reduce it by ~82%.
+
+Setup:
+    pip install agentops openai
+    pipx install uncommon-route
+    uncommon-route init
+    uncommon-route serve
+
+Environment variables:
+    AGENTOPS_API_KEY - your AgentOps API key
+    OPENAI_API_KEY - your OpenAI API key (used by UncommonRoute upstream)
+    ANTHROPIC_API_KEY - your Anthropic API key (optional, for more routing options)
+"""
+
+from openai import OpenAI
+import agentops
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_api_key_here")
+
+# --- Initialize AgentOps FIRST (before any LLM calls) ---
+agentops.init(
+    auto_start_session=True,
+    trace_name="UncommonRoute Cost Optimization",
+    tags=["uncommonroute", "cost-optimization", "agentops-example"],
+)
+
+tracer = agentops.start_trace(
+    trace_name="UncommonRoute Cost Optimization",
+    tags=["uncommonroute", "cost-optimization"],
+)
+
+# --- Point the OpenAI client at UncommonRoute's local proxy ---
+# UncommonRoute runs on localhost:8403 and is fully OpenAI-compatible.
+# It analyzes each request and routes to the cheapest capable model.
+client = OpenAI(
+    base_url="http://localhost:8403/v1",
+    api_key="not-needed",  # UR uses your provider keys configured during `uncommon-route init`
+)
+
+# --- Task 1: Simple greeting (routed to a cheap/fast model) ---
+print("=== Task 1: Simple greeting ===")
+response = client.chat.completions.create(
+    model="uncommon-route/auto",
+    messages=[{"role": "user", "content": "Say hello in three languages."}],
+)
+print(f"Response: {response.choices[0].message.content}\n")
+
+# --- Task 2: Medium complexity (routed to a mid-tier model) ---
+print("=== Task 2: Code explanation ===")
+response = client.chat.completions.create(
+    model="uncommon-route/auto",
+    messages=[
+        {
+            "role": "user",
+            "content": "Explain the difference between a mutex and a semaphore. Give a short code example in Python.",
+        }
+    ],
+)
+print(f"Response: {response.choices[0].message.content}\n")
+
+# --- Task 3: Complex reasoning (routed to a premium model) ---
+print("=== Task 3: Architecture design ===")
+response = client.chat.completions.create(
+    model="uncommon-route/auto",
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a senior distributed systems architect.",
+        },
+        {
+            "role": "user",
+            "content": (
+                "Design a rate limiter for a multi-region API gateway that handles "
+                "100k requests/second. Consider consistency vs availability tradeoffs, "
+                "clock skew, and burst handling. Provide a detailed architecture."
+            ),
+        },
+    ],
+)
+print(f"Response: {response.choices[0].message.content}\n")
+
+# --- End trace and show results ---
+agentops.end_trace(tracer, end_state="Success")
+
+print("=" * 60)
+print("Check your AgentOps dashboard to see:")
+print("  - Cost breakdown per request")
+print("  - Which model UncommonRoute selected for each task")
+print("  - Total spend vs. what it would have cost with a single premium model")
+print("=" * 60)
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