ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter2/prompt-engineering/GPT5_CONFIGURATION.md

@@ -0,0 +1,91 @@
+# GPT-5 Configuration Guide for tau-bench
+
+## Overview
+GPT-5 (via OpenRouter) uses internal "thinking" tokens similar to OpenAI's o1 models. This can result in high token usage if not properly configured.
+
+## Key Configuration
+
+### 1. Model and Provider
+```python
+model = "openai/gpt-5"
+provider = "openrouter"  # Automatically set in our configuration
+```
+
+### 2. Minimize Thinking Tokens
+Use `reasoning_effort` parameter via `extra_body`:
+
+```python
+from litellm import completion
+
+response = completion(
+    model="openai/gpt-5",
+    custom_llm_provider="openrouter",
+    messages=messages,
+    temperature=1.0,  # GPT-5 only supports 1.0
+    extra_body={"reasoning_effort": "low"}  # Critical for efficiency
+)
+```
+
+### 3. Reasoning Effort Levels
+- **"low"**: Minimal thinking tokens (~7-333 completion tokens)
+- **"medium"**: Moderate thinking (~7-500 completion tokens)  
+- **"high"**: Deep thinking (~71-1500+ completion tokens)
+- **Not specified**: Defaults to variable, often high usage
+
+## Token Usage Examples
+
+| Task | Without reasoning_effort | With "low" | Savings |
+|------|-------------------------|------------|---------|
+| Simple greeting | 1358 tokens | 333 tokens | 75% |
+| Math (2+2) | 7-71 tokens | 7 tokens | 90% |
+| Complex reasoning | 2000+ tokens | 500-800 tokens | 60-75% |
+
+## Implementation in tau-bench
+
+The ablation agent now automatically sets `reasoning_effort="low"` for GPT-5:
+
+```python
+# In ablation_agent.py
+if "gpt-5" in self.model:
+    completion_kwargs["extra_body"] = {"reasoning_effort": "low"}
+```
+
+## Environment Variables
+
+```bash
+# Required for OpenRouter
+export OPENROUTER_API_KEY="your_key"
+
+# Optional debugging
+export DEBUG_API_CALLS="true"  # Show API call details
+export LITELLM_LOG="DEBUG"     # Show litellm internals
+```
+
+## Testing Tools
+
+1. **Direct API test**: `python test_openrouter_direct.py`
+2. **Reasoning comparison**: `python test_reasoning_effort.py`
+3. **Single task debug**: `./test_single_task.sh`
+4. **Full debug run**: `./debug_run.sh`
+
+## Best Practices
+
+1. **Always use `reasoning_effort="low"`** for tau-bench experiments unless you specifically need deep reasoning
+2. **Monitor token usage** in the debug output to catch any issues
+3. **Use temperature=1.0** (GPT-5 requirement)
+4. **Batch similar tasks** to amortize thinking overhead
+
+## Troubleshooting
+
+If you see high token usage:
+1. Check that `reasoning_effort="low"` is being passed
+2. Verify it's in `extra_body` not as a direct parameter
+3. Look for the "💭 Using reasoning_effort='low'" message in debug output
+4. Consider the prompt complexity - very complex prompts may still use more tokens
+
+## Cost Implications
+
+With `reasoning_effort="low"`:
+- ~75% reduction in token costs for typical tau-bench tasks
+- Faster response times
+- More consistent token usage across tasks