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Fix empty tool-use response handling

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Haoran
2026-08-15 00:03:45 +08:00
parent 985456f4ad
commit 168fff86dd
90 changed files with 885 additions and 503 deletions
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50
s01_agent_loop/README.md
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@@ -25,12 +25,12 @@ Every round-trip, you're the middle layer. Automating that is what this chapter
![Agent Loop](images/agent-loop.en.svg)
A `while True` loop: keep going when the model calls a tool, stop when it doesn't. The entire process hinges on two signals:
A `while True` loop: keep going when the model calls a tool, stop when it doesn't. The loop checks the response content blocks directly:
| Signal | Meaning | Loop Action |
|--------|---------|-------------|
| `stop_reason == "tool_use"` | Model raises hand: "I need a tool" | Execute → feed result back → continue |
| `stop_reason != "tool_use"` | Model says: "I'm done" | Exit loop |
| Contains a `tool_use` block | Model requests a tool call | Execute → feed result back → continue |
| Contains no `tool_use` block | Model did not call a tool | Exit loop |
---
@@ -57,22 +57,26 @@ response = client.messages.create(
```python
messages.append({"role": "assistant", "content": response.content})
if response.stop_reason != "tool_use":
tool_calls = [
block for block in response.content if block.type == "tool_use"
]
if not tool_calls:
return
```
Only concrete `tool_use` blocks enter the execution stage, so the loop never appends an empty tool-result message.
**Step 4**: Execute the tool the model requested and collect the results.
```python
results = []
for block in response.content:
if block.type == "tool_use":
output = run_bash(block.input["command"])
results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
})
for block in tool_calls:
output = run_bash(block.input["command"])
results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
})
```
**Step 5**: Append the tool results as a new message and go back to Step 2.
@@ -92,22 +96,24 @@ def agent_loop(messages):
)
messages.append({"role": "assistant", "content": response.content})
if response.stop_reason != "tool_use":
tool_calls = [
block for block in response.content if block.type == "tool_use"
]
if not tool_calls:
return
results = []
for block in response.content:
if block.type == "tool_use":
output = run_bash(block.input["command"])
results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
})
for block in tool_calls:
output = run_bash(block.input["command"])
results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
})
messages.append({"role": "user", "content": results})
```
Under 30 lines — that's the minimal runnable agent harness kernel. It's not intelligence itself, but the smallest runtime framework that lets the model keep acting. The model decides (whether to call a tool, which one), the harness executes (calls the tool and appends the result as a new message). The next 16 chapters all add mechanisms on top of this loop. The loop itself never changes.
Just over 30 lines — that's the minimal runnable agent harness kernel. It's not intelligence itself, but the smallest runtime framework that lets the model keep acting. The model decides (whether to call a tool, which one), the harness executes (calls the tool and appends the result as a new message). The next 16 chapters all add mechanisms on top of this loop. The loop itself never changes.
---