Support Claude Code as LitAgent

examples/claude_code/README.md

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+# Training ANY LLM to Claude Code
+
+This example wraps Claude Code as the agent to:
+1. collect traces from agent execution on coding tasks;
+2. train a hosted LLM with the traces ***🔨 Under development***
+
+## Requirements
+1. install [agentlightning](https://microsoft.github.io/agent-lightning/stable/tutorials/installation/)
+2. `(uv) pip install swebench` for evaluation
+
+## Dataset
+We provide a small dataset `swe_debug.jsonl` which is a subset of [SWE-bench](https://huggingface.co/datasets/SWE-bench/SWE-bench) for sanity check.
+
+## Included Files
+
+| Filename                        | Description |
+|--------------------------------|-------------|
+| `cc_agent.py`                   | Main entry point for running Claude Code agent on coding tasks with trace collection capabilities |
+| `claude_code_controller.py`     | Controller implementation for managing Claude Code agent interactions and execution |
+| `custom_adapter.py`             | Custom adapter for integrating with Claude Code's interface and communication protocols |
+| `custom_callbacks.py`           | Callback handlers for customizing agent behavior and responses during execution |
+| `handle_hook.template.sh`       | Template script for handling hooks during agent execution |
+| `settings.template.json`        | Template configuration file with default settings for Claude Code agent |
+| `swe_debug.jsonl`               | Debug dataset containing a subset of SWE-bench samples for testing and verification |
+| `swebench_utils/`               | Utility module with helper functions for SWE-bench dataset containerized exeuction and evaluation |
+
+## Trace collection
+We support running Claude Code via two ways:
+- Hosted LLM servers, supporting versatile customizations
+- Official Claude Code
+
+### From Hosted LLM server
+1. Prepare an OpenAI-compatible server:
+```bash
+vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct \
+    --max-model-len 131072 \
+    --enable-auto-tool-choice \
+    --tool-call-parser qwen3_coder
+```
+
+2. Sanity check:
+```bash
+cd examples/cc
+
+# Suppose the vllm server is running at localhost
+python cc_agent \
+    --model_name_or_path Qwen/Qwen3-Coder-30B-A3B-Instruct \
+    --server_address http://localhost:8000/v1 \
+    --dataset_path swe_debug.jsonl \
+    --max_step 32 \
+    --output_dir data_debug
+```
+The above commands will generate a `data_debug` dir, which contains two targets: (1) a Huggingface Dataset named `dataset-<instance_id>` and (2) a trace file named `stream_<instance_id>.jsonl`, where `instance_id` is a unique key of the SWE-bench samples.
+The dataset showcases the versatile customization capability of agent-lightning. In particular, we support extracting **prompt/response ids**, **logprobs** from the vllm server.
+The trace file is the conversation logs for claude code to tackle the SWE-bench instance.
+
+In addition, there will be a `logs` dir, which is the output of the docker container executing agent calls.
+
+### From official Claude Code
+1. Prepare ANTHROPIC_API_KEY
+```bash
+export ANTHROPIC_API_KEY=sk-<your private key>
+```
+
+2. Sanity check
+```bash
+cd examples/cc
+python cc_agent \
+    --official \
+    --dataset_path swe_debug.jsonl \
+    --max_step 32 \
+    --output_dir data_debug
+```
+As the underlying model is provided by Anthropic, we cannot obtain prompt/response ids and logprobs. However, we can still obtain a trace file named `<instance_id>.json` under `data_debug`.