Tool calls on C++ engines

The official template iterates tool call arguments with |items:

{%- for key, value in tool_call.arguments|items %}

Python's Jinja supports |items. C++ runtimes (LM Studio, llama.cpp, MLX) do not — the template produces a rendering error instead of output. This template uses direct dictionary key lookups instead:

{%- for args_name in tool_call.arguments %}
    {%- set args_value = tool_call.arguments[args_name] %}

It also replaces is sequence with is iterable (stricter C++ runtimes require it), removes |safe wrappers (also Python-only), and handles arguments returned as raw strings instead of objects.

developer role

The OpenAI-compatible API spec sends message.role == "developer" for system-level instructions. The official Qwen template only checks for "system" and throws on anything else. Both templates here accept "developer" and map it to the system role.

Empty thinking blocks

The official template wraps every past assistant turn in thinking tags:

<|im_start|>assistant
<think/>
</think >

Here is the answer...

When there's no reasoning content, those tags are dead weight — they waste context tokens and break prefix caching. The Qwen 3.5 template checks reasoning_content before emitting. The Qwen 3.6 template goes further: it respects the preserve_thinking kwarg, checks reasoning_content|trim|length > 0, and ties history visibility to the <|think_off|> override.

</thinking> hallucination (Qwen 3.6 only)

The Qwen 3.6 model sometimes generates </thinking> instead of the expected closing tag. The official parser splits on </think > only and fails. The 3.6 template detects which closing tag was actually used and splits on that:

{%- if '</think >' in content %}
    {%- set think_end_token = '</think >' %}
{%- elif '</thinking>' in content %}
    {%- set think_end_token = '</thinking>' %}

It also handles interrupted generation (max tokens hit mid-thought) by rescuing incomplete streams instead of injecting broken tag pairs.

Arguments serialization

The official template serializes argument values with |tojson unconditionally, which turns Python True into JSON true correctly but fails when the value is already a string. The fixed templates check the type first — strings pass through as-is, everything else goes through |tojson.

No-user-query exception

The official template scans the message list in reverse to find the last "real" user query (skipping tool-result wrappers). If all user messages are tool results — or there are no user messages at all — it fires raise_exception('No user query found in messages.') and the template hard-crashes.

This breaks real usage:

• Agentic tool-calling chains where the conversation ends with tool results and no fresh user query
• After /reset or /new in runtimes like OpenClaw, where tool results from a prior session persist without a new user message
• System-only contexts with no user messages

The fix replaces the exception with a graceful fallback: {%- set ns.last_query_index = messages|length - 1 %}. The thinking display logic then degrades naturally — assistant turns with reasoning content still show thinking tags when preserve_thinking is enabled.