diff --git a/tests/llm_proxy/test_text_content.py b/tests/llm_proxy/test_text_content.py
new file mode 100644
index 000000000..c011460d4
--- /dev/null
+++ b/tests/llm_proxy/test_text_content.py
@@ -0,0 +1,156 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import json
+import sys
+from ast import literal_eval
+from typing import Any, Dict, List, Sequence, Type, Union, cast
+
+sys.path.append("examples/claude_code")
+
+import anthropic
+import openai
+import pytest
+from litellm.integrations.custom_logger import CustomLogger
+from portpicker import pick_unused_port
+from swebench.harness.constants import SWEbenchInstance
+from swebench.harness.utils import load_swebench_dataset  # pyright: ignore[reportUnknownVariableType]
+from transformers import AutoTokenizer
+
+from agentlightning import LitAgentRunner, OtelTracer
+from agentlightning.llm_proxy import LLMProxy, _reset_litellm_logging_worker  # pyright: ignore[reportPrivateUsage]
+from agentlightning.store import LightningStore, LightningStoreServer, LightningStoreThreaded
+from agentlightning.store.memory import InMemoryLightningStore
+from agentlightning.types import LLM, Span
+from examples.claude_code.claude_code_agent import ClaudeCodeAgent, _load_dataset
+
+from ..common.tracer import clear_tracer_provider
+
+pytest.skip(reason="Debug only", allow_module_level=True)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "otlp_enabled",
+    [
+        True,
+    ],
+)
+async def test_claude_code(otlp_enabled: bool):
+    # For unknown reasons, I don't have local machine for debugging,
+    # this model is deployed remotely, so as the whole unit test.
+    model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
+    endpoint = "http://localhost:8000/v1"
+    max_turns = 5
+
+    clear_tracer_provider()
+    _reset_litellm_logging_worker()  # type: ignore
+
+    # Prepare utilities for testing
+    dataset_path = "examples/claude_code/swebench_samples.jsonl"
+    instance = _load_dataset(dataset_path, limit=1)[0]
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    # Load full swebench dataset. Mainly for evaluation purposes.
+    swebench_full_dataset = load_swebench_dataset("princeton-nlp/SWE-bench", split="test")
+    # Initialize Claude Code Agent
+    claude_code_agent = ClaudeCodeAgent(swebench_full_dataset=swebench_full_dataset, max_turns=max_turns)
+
+    system_prompt_piece = "Please do not commit your edits. We will do it later."
+
+    # Initialize agl Infrastructure
+    inmemory_store = InMemoryLightningStore()
+    if otlp_enabled:
+        store = LightningStoreServer(store=inmemory_store, host="127.0.0.1", port=pick_unused_port())
+        await store.start()
+    else:
+        store = LightningStoreThreaded(inmemory_store)
+
+    proxy = LLMProxy(
+        model_list=[
+            {
+                "model_name": "claude-sonnet-4-5-20250929",
+                "litellm_params": {
+                    "model": "hosted_vllm/" + model_name,
+                    "api_base": endpoint,
+                },
+            },
+            {
+                "model_name": "claude-haiku-4-5-20251001",
+                "litellm_params": {
+                    "model": "hosted_vllm/" + model_name,
+                    "api_base": endpoint,
+                },
+            },
+        ],
+        launch_mode="thread" if not otlp_enabled else "mp",
+        port=pick_unused_port(),
+        store=store,
+    )
+    proxy.server_launcher._access_host = "localhost"
+    await proxy.start()
+
+    rollout = await store.start_rollout(None)
+
+    resource = proxy.as_resource(rollout.rollout_id, rollout.attempt.attempt_id, model="local")
+    print(f">>> DEBUG: {proxy.server_launcher.access_endpoint=}")
+    print(f">>> DEBUG: {resource.endpoint=}, {resource.model=}")
+
+    # Dry run to generate spans
+    await claude_code_agent.rollout_async(
+        task=instance,
+        resources={"llm": resource},
+        rollout=rollout,
+    )
+
+    spans = await store.query_spans(rollout.rollout_id)
+
+    # Preprocess raw spans
+    valid_spans = []
+    for span in spans:
+        if span.name != "raw_gen_ai_request":
+            continue
+
+        prompt_ids = span.attributes["llm.hosted_vllm.prompt_token_ids"]
+        prompt_text = tokenizer.decode(literal_eval(prompt_ids))
+        if system_prompt_piece not in prompt_text:
+            continue
+
+        choice = literal_eval(span.attributes["llm.hosted_vllm.choices"])[0]
+        response_ids = choice["token_ids"]
+        response_text = tokenizer.decode(response_ids)
+
+        prompt_messages = literal_eval(span.attributes["llm.hosted_vllm.messages"])
+        response_message = choice["message"]
+
+        valid_spans.append(
+            {
+                "prompt_text": prompt_text,
+                "response_text": response_text,
+                "prompt_messages": prompt_messages,
+                "response_message": response_message,
+            }
+        )
+
+    with open("logs/test_spans.jsonl", "w") as f:
+        for span in spans:
+            f.write(json.dumps(span.model_dump(), indent=2) + "\n")
+
+    with open("logs/test_valid_spans.jsonl", "w") as f:
+        for span in valid_spans:
+            f.write(json.dumps(span, indent=2) + "\n")
+
+    # Test case 1: At least two valid spans
+    assert len(valid_spans) > 1
+    print(f"Generated {len(spans)} spans with {len(valid_spans)} LLM requests.")
+
+    # Test case 2:
+    for i in range(1, len(valid_spans)):
+        prev = valid_spans[i - 1]
+        curr = valid_spans[i]
+
+        # The current prompt should contain the previous response
+        assert prev["response_text"] in curr["prompt_text"]
+
+    await proxy.stop()
+    if isinstance(store, LightningStoreServer):
+        await store.stop()