diff --git a/examples/zero-code-examples/anthropic/requirements.txt b/examples/zero-code-examples/anthropic/requirements.txt
new file mode 100644
index 0000000..3b54f72
--- /dev/null
+++ b/examples/zero-code-examples/anthropic/requirements.txt
@@ -0,0 +1,6 @@
+anthropic>=0.40.0
+
+opentelemetry-sdk>=1.36.0
+opentelemetry-exporter-otlp-proto-http>=1.36.0
+opentelemetry-instrumentation-anthropic>=0.40.0
+python-dotenv>=1.0.0
diff --git a/examples/zero-code-examples/anthropic/run.py b/examples/zero-code-examples/anthropic/run.py
new file mode 100644
index 0000000..19f9fad
--- /dev/null
+++ b/examples/zero-code-examples/anthropic/run.py
@@ -0,0 +1,150 @@
+"""Run an Anthropic Claude dice agent with standard OTLP export — no agentevals SDK.
+
+Demonstrates zero-code integration: any OTel-instrumented agent streams
+traces and logs to agentevals by pointing the OTLP exporter at the receiver.
+
+The only agentevals-specific setup is the OTLP endpoint and resource
+attributes. The agent code itself is unchanged.
+
+Prerequisites:
+    1. pip install -r requirements.txt
+    2. agentevals serve --dev
+    3. export ANTHROPIC_API_KEY="your-key-here"
+
+Usage:
+    python examples/zero-code-examples/anthropic/run.py
+"""
+
+import json
+import os
+import random
+
+import anthropic
+from dotenv import load_dotenv
+from opentelemetry import trace
+from opentelemetry._logs import set_logger_provider
+from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+from opentelemetry.instrumentation.anthropic import AnthropicInstrumentor
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+
+load_dotenv(override=True)
+
+TOOLS = [
+    {
+        "name": "roll_die",
+        "description": "Roll a single die with the given number of sides and return the result.",
+        "input_schema": {
+            "type": "object",
+            "properties": {"n_sides": {"type": "integer", "description": "Number of sides on the die."}},
+            "required": ["n_sides"],
+        },
+    },
+    {
+        "name": "check_prime",
+        "description": "Return true if the given number is prime, false otherwise.",
+        "input_schema": {
+            "type": "object",
+            "properties": {"number": {"type": "integer", "description": "The number to check."}},
+            "required": ["number"],
+        },
+    },
+]
+
+
+def roll_die(n_sides: int) -> int:
+    return random.randint(1, n_sides)
+
+
+def check_prime(number: int) -> bool:
+    if number < 2:
+        return False
+    for i in range(2, int(number**0.5) + 1):
+        if number % i == 0:
+            return False
+    return True
+
+
+def run_agent(client: anthropic.Anthropic, query: str) -> str:
+    messages = [{"role": "user", "content": query}]
+
+    while True:
+        response = client.messages.create(
+            model="claude-haiku-4-5-20251001",
+            max_tokens=1024,
+            tools=TOOLS,
+            messages=messages,
+        )
+
+        if response.stop_reason == "end_turn":
+            for block in response.content:
+                if hasattr(block, "text"):
+                    return block.text
+            return ""
+
+        tool_results = []
+        for block in response.content:
+            if block.type == "tool_use":
+                if block.name == "roll_die":
+                    result = roll_die(**block.input)
+                elif block.name == "check_prime":
+                    result = check_prime(**block.input)
+                else:
+                    result = "Unknown tool"
+                tool_results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(result)})
+
+        messages.append({"role": "assistant", "content": response.content})
+        messages.append({"role": "user", "content": tool_results})
+
+
+def main():
+    if not os.getenv("ANTHROPIC_API_KEY"):
+        print("ANTHROPIC_API_KEY not set.")
+        return
+
+    endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318")
+    print(f"OTLP endpoint: {endpoint}")
+
+    os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "true"
+
+    os.environ.setdefault(
+        "OTEL_RESOURCE_ATTRIBUTES",
+        "agentevals.eval_set_id=anthropic_agent_eval,agentevals.session_name=anthropic-zero-code",
+    )
+
+    resource = Resource.create()
+
+    tracer_provider = TracerProvider(resource=resource)
+    tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(), schedule_delay_millis=1000))
+    trace.set_tracer_provider(tracer_provider)
+
+    logger_provider = LoggerProvider(resource=resource)
+    logger_provider.add_log_record_processor(BatchLogRecordProcessor(OTLPLogExporter(), schedule_delay_millis=1000))
+    set_logger_provider(logger_provider)
+
+    AnthropicInstrumentor().instrument()
+
+    client = anthropic.Anthropic()
+
+    test_queries = [
+        "Roll a 20-sided die for me",
+        "Is the number you rolled prime?",
+    ]
+
+    for i, query in enumerate(test_queries, 1):
+        print(f"\n[{i}/{len(test_queries)}] User: {query}")
+        response = run_agent(client, query)
+        print(f"     Agent: {response}")
+
+    print()
+    tracer_provider.force_flush()
+    logger_provider.force_flush()
+    print("All traces and logs flushed to OTLP receiver.")
+
+
+if __name__ == "__main__":
+    main()