bug: repeated context compaction can drop every user message and break Qwen/vLLM

Summary

On cptr 0.9.12, a long agentic Chat Completions task can fail after repeated automatic context compaction when the retained keep_zone contains only assistant and tool messages.

With a Qwen model served by vLLM through LiteLLM, the next request fails with:

litellm.BadRequestError: Hosted_vllmException -
{"error":{"message":"No user query found in messages.","type":"BadRequestError","code":400}}

Raising chat.compact_token_threshold only delays the failure.

Environment

• Computer/cptr: 0.9.12
• Connection: OpenAI-compatible Chat Completions
• Gateway: LiteLLM hosted_vllm
• Backend: vLLM
• Model group: Qwen
• OS: Windows (but the compaction logic is platform-independent)

Reproduction

1. Start a task from one normal user request.
2. Let the agent execute a long sequence of tool calls and tool results.
3. Allow automatic compaction to run more than once.
4. On a later compaction, the only retained user message can fall into the new drop zone.
5. The next upstream request contains a system prompt/summary followed only by assistant/tool turns.

A sanitized real trace reproduced the second compaction as:

messages before split: 263
split index:           158
messages kept:         105
user messages kept:    0

Root cause

In cptr/utils/chat_task.py:

• _find_safe_split() protects tool-call/tool-result boundaries and keeps at least two messages.
• It does not guarantee that keep_zone contains a top-level user turn.
• After compaction, messages = keep_zone.
• api_messages is then sent to the provider unchanged with respect to roles.

For a task that began with one user prompt followed by many assistant/tool iterations, repeated 40% retention can eventually remove every user turn.

Expected behavior

Every compacted continuation sent through Chat Completions should contain a real top-level user anchor, while preserving the retained assistant/tool ordering.

Suggested fix

Add a small invariant immediately before constructing ChatCompletionForm:

def _ensure_user_query(messages: list[dict]) -> list[dict]:
    if any(message.get("role") == "user" for message in messages):
        return messages
    return [
        {
            "role": "user",
            "content": (
                "Continue the task using the conversation summary "
                "and the tool results below."
            ),
        },
        *messages,
    ]

# After removing internal message IDs:
api_messages = _ensure_user_query(api_messages)

This keeps normal histories unchanged and adds one synthetic continuation turn only when compaction removed every user message.

Regression tests

1. A history that already has a user message is returned unchanged.
2. An assistant/tool-only history gets one non-empty user message prepended.
3. Existing assistant/tool messages retain their exact order and content.
4. Repeated compaction of a single-user agentic task still produces a provider request containing a user turn.

Start in cptr/utils/chat_task.py by reading _find_safe_split and the code that removes internal message IDs before constructing ChatCompletionForm. Verify the existing compaction flow and add regression coverage for user-preserving, assistant/tool-only, ordering, and repeated-compaction cases. Done means every compacted Chat Completions request contains a top-level user turn without changing histories that already have one.