Improve session controls and compact LLM logs

frontends/conductor.py

@@ -42,6 +42,20 @@ def _apply_desktop_model(agent: "GenericAgent") -> None:
         print(f"[conductor] failed to apply desktop model #{no}: {e}", file=sys.stderr)
 
 
+def _select_llm(agent: "GenericAgent", llm: Any) -> bool:
+    if llm is None or str(llm).strip() == "": return False
+    q = str(llm).strip()
+    if isinstance(llm, int) or q.isdigit(): agent.next_llm(int(q)); return True
+    q = q.lower(); agent.load_llm_sessions()
+    for i, c in enumerate(agent.llmclients):
+        vals = []
+        for fn in (lambda: agent.get_llm_name(c), lambda: agent.get_llm_name(c, model=True), lambda: c.backend.name, lambda: c.backend.model):
+            try: vals.append(str(fn()).lower())
+            except Exception: pass
+        if any(q in v for v in vals): agent.next_llm(i); return True
+    raise ValueError(f"llm not found: {llm}")
+
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # 服务启动（事件循环已就绪）：捕获 loop 供工作线程跨线程推 WS 广播，并起主agent
@@ -62,6 +76,7 @@ class ChatIn(BaseModel):
 
 class StartSubagentIn(BaseModel):
     prompt: str
+    llm: Any = None
 
 class ApprovalIn(BaseModel):
     prompt: str
@@ -70,6 +85,7 @@ class ApprovalIn(BaseModel):
 class SubagentActionIn(BaseModel):
     action: str = "intervene"  # intervene | abort | kill
     msg: str = ""
+    llm: Any = None
 
 @dataclass
 class SubAgentState:
@@ -215,13 +231,13 @@ class SubagentPool:
                 s.agent.task_queue.put("EXIT")  
                 with self.lock: self.subagents.pop(sid, None)  
             if to_abort: push_cards()
-    def start_subagent(self, prompt: str) -> dict:
+    def start_subagent(self, prompt: str, llm: Any = None) -> dict:
         sid = short_id()
         agent = GenericAgent()
         agent.inc_out = True
         agent.verbose = False
         agent.no_print = True
-        _apply_desktop_model(agent)
+        if not _select_llm(agent, llm): _apply_desktop_model(agent)
         th = start_agent_runner(agent, f"subagent-{sid}")
         state = SubAgentState(id=sid, agent=agent, prompt=prompt, status="running", thread=th)
         with self.lock: self.subagents[sid] = state
@@ -233,10 +249,11 @@ class SubagentPool:
         threading.Thread(target=monitor_display_queue, args=(sid, dq, True), name=f"monitor-{sid}", daemon=True).start()
         push_cards()
         return {"id": sid, "status": "running"}
-    def input_subagent(self, sid: str, msg: str) -> dict:
+    def input_subagent(self, sid: str, msg: str, llm: Any = None) -> dict:
         with self.lock: s = self.subagents.get(sid)
         if not s: return {"error": "subagent not found", "id": sid}
         if s.status == "running": return {"error": "subagent is still running, cannot input/reply. Start a new subagent instead.", "id": sid}
+        _select_llm(s.agent, llm)
         s.prompt = msg
         s.reply = ""
         s.status = "running"
@@ -257,10 +274,10 @@ READMES = {
 Conductor API\tBase: http://{HOST}:{PORT}
 
 POST /chat\tbody: {{"msg": "..."}}\t给用户发消息
-POST /subagent\tbody: {{"prompt": "..."}}\t启动新subagent，返回 {{"id": "xxx"}}
+POST /subagent\tbody: {{"prompt": "..."}}\t启动新subagent，返回 {{"id": "xxx"}}；指定模型加参数llm(数字/名称)
 POST /approval\tbody: {{"prompt": "...", "source": "..."}}\t推一条待批任务到前端(后端不存)，用户同意则直接派发为subagent
 POST /subagent/{{id}}\tbody: {{"action": "keyinfo", "msg": "..."}}\t注入key_info（agent下轮可见）
-POST /subagent/{{id}}\tbody: {{"action": "input", "msg": "..."}}\t开新一轮任务（agent停下后追加）
+POST /subagent/{{id}}\tbody: {{"action": "input", "msg": "..."}}\t开新一轮任务；指定模型加参数llm(数字/名称)
 POST /subagent/{{id}}\tbody: {{"action": "stop"}}\t中断执行但保留（可继续input/reply）
 GET /chat?last=N\t返回最近N条对话（默认20）
 GET /subagent\t返回 {{"items": [...]}}\t查看所有subagent状态
@@ -452,7 +469,7 @@ INSTR_DISPATCHED = "Task received. I'll handle THIS TASK from here. You MUST to
 
 @app.post("/subagent")
 def api_start_subagent(body: StartSubagentIn):
-    result = pool.start_subagent(body.prompt)
+    result = pool.start_subagent(body.prompt, body.llm)
     result["instruction"] = INSTR_DISPATCHED
     return result
 
@@ -466,7 +483,7 @@ def api_subagent_action(sid: str, body: SubagentActionIn):
         result["instruction"] = "Received. I'll incorporate this. You MUST to do other task or end your reply."
         return result
     if action in ("input", "reply", "append", "message", "msg"):
-        result = pool.input_subagent(sid, body.msg)
+        result = pool.input_subagent(sid, body.msg, body.llm)
         result["instruction"] = INSTR_DISPATCHED
         return result
     if action in ("abort", "stop"):
@@ -479,10 +496,11 @@ def api_subagent_action(sid: str, body: SubagentActionIn):
 
 @app.get("/chat")
 def api_get_chat(last: int = 20):
+    items = [m.copy() for m in chat_messages[-last:]]
     for m in chat_messages:
         if m.get("role") == "user" and not m.get("read"): m["read"] = True
     schedule_broadcast({"type": "chat_read"})
-    return {"items": chat_messages[-last:]}
+    return {"items": items}
 
 @app.post("/chat")
 def api_chat(body: ChatIn):

frontends/worldline.py

@@ -1081,10 +1081,9 @@ def render_native_history(history, ts=None):
     `_parse_native_history(_pairs(render_native_history(h))) == h`.
 
     Byte-for-byte matches llmcore's `_write_llm_log` for the tool backend:
-    Prompt = `json.dumps(merged, ensure_ascii=False, indent=2)`; Response =
-    `str(content_blocks)` which for a list equals `repr(...)` (survives
-    True/False/None that JSON would emit as true/false/null and break
-    `ast.literal_eval`). Only complete (user, assistant) pairs are emitted."""
+    Prompt/Response each put one content block per line, with their wrappers
+    attached to the first/last lines. Response keeps Python repr so
+    True/False/None survive `ast.literal_eval`. Only complete pairs are emitted."""
     ts = ts or datetime.now().strftime('%Y-%m-%d %H:%M:%S')
     out, i, n = [], 0, len(history)
     while i < n:
@@ -1092,8 +1091,13 @@ def render_native_history(history, ts=None):
         if isinstance(u, dict) and u.get('role') == 'user' and i + 1 < n:
             a = history[i + 1]
             if isinstance(a, dict) and a.get('role') == 'assistant':
-                out.append(f"=== Prompt === {ts}\n{json.dumps(u, ensure_ascii=False, indent=2)}\n\n")
-                out.append(f"=== Response === {ts}\n{a.get('content')!r}\n\n")
+                prompt_blocks = u.get('content', [])
+                prompt_raw = '{"role": "user", "content": [\n' + ",\n".join(
+                    json.dumps(b, ensure_ascii=False) for b in prompt_blocks) + "]}"
+                out.append(f"=== Prompt === {ts}\n{prompt_raw}\n\n")
+                blocks = a.get('content', [])
+                raw = "[" + ",\n".join(repr(b) for b in blocks) + "]"
+                out.append(f"=== Response === {ts}\n{raw}\n\n")
                 i += 2
                 continue
         i += 1

llmcore.py

@@ -557,6 +557,7 @@ class BaseSession:
         self.service_tier = _enum('service_tier', {'auto', 'default', 'priority', 'flex'})
         self.thinking_type = _enum('thinking_type', {'adaptive', 'enabled', 'disabled'})
         self.thinking_budget_tokens = cfg.get('thinking_budget_tokens')
+        self.omit_thinking = cfg.get('omit_thinking', False)  # Exclude thinking from session history
         mode = str(cfg.get('api_mode', 'chat_completions')).strip().lower().replace('-', '_')
         self.api_mode = 'responses' if mode in ('responses', 'response') else 'chat_completions'
         self.temperature = cfg.get('temperature', 1)
@@ -735,7 +736,9 @@ class NativeClaudeSession(BaseSession):
         except StopIteration as e: content_blocks = e.value or []
         if content_blocks and (_injected := _ensure_text_block(content_blocks)): yield _injected
         if content_blocks and not (len(content_blocks) == 1 and content_blocks[0].get("text", "").startswith("!!!Error:")):
-            self.history.append({"role": "assistant", "content": content_blocks})
+            history_blocks = content_blocks
+            if self.omit_thinking: history_blocks = [b for b in content_blocks if b.get("type") != "thinking"]
+            self.history.append({"role": "assistant", "content": history_blocks})
         text_parts = [b["text"] for b in content_blocks if b.get("type") == "text"]
         content = "\n".join(text_parts).strip()
         tool_calls = [MockToolCall(b["name"], b.get("input", {}), id=b.get("id", "")) for b in content_blocks if b.get("type") == "tool_use"]
@@ -748,7 +751,8 @@ class NativeClaudeSession(BaseSession):
             if think_match:
                 thinking = think_match.group(1).strip()
                 content = re.sub(think_pattern, "", content, flags=re.DOTALL)
-        return MockResponse(thinking, content, tool_calls, str(content_blocks))
+        raw = "[" + ",\n".join(repr(b) for b in content_blocks) + "]"
+        return MockResponse(thinking, content, tool_calls, raw)
 
 class NativeOAISession(NativeClaudeSession):
     native_ua = "codex_exec/0.139.0 (Windows 10.0.26200; x86_64) unknown (codex_exec; 0.139.0)"
@@ -1060,7 +1064,8 @@ class NativeToolClient:
         final_content = tool_result_blocks + filtered_content
         if not final_content: final_content = [{"type": "text", "text": "."}]
         merged = {"role": "user", "content": final_content}
-        _write_llm_log('Prompt', json.dumps(merged, ensure_ascii=False, indent=2), self.log_path)
+        prompt_raw = '{"role": "user", "content": [\n' + ",\n".join(json.dumps(b, ensure_ascii=False) for b in final_content) + "]}"
+        _write_llm_log('Prompt', prompt_raw, self.log_path)
         gen = self.backend.ask(merged)
         try:
             while True: