chore: self-commit core logic updates and agent state

agentapp.py

@@ -10,7 +10,6 @@ import time, json, re
 with open('tools_schema.json', 'r', encoding='utf-8') as f:
     TOOLS_SCHEMA = json.load(f)
 
-
 st.set_page_config(page_title="Cowork", layout="wide")
 
 from sidercall import SiderLLMSession, LLMSession, ToolClient
@@ -25,7 +24,7 @@ def init():
 
 llmclient = init()
 
-from ga import GenericAgentHandler, smart_format
+from ga import GenericAgentHandler, smart_format, get_global_memory
 
 def get_system_prompt():
     if not os.path.exists('memory'): os.makedirs('memory')
@@ -33,50 +32,16 @@ def get_system_prompt():
         with open('memory/global_mem.txt', 'w', encoding='utf-8') as f: f.write('')
     if not os.path.exists('memory/global_mem_insight.txt'):
         with open('memory/global_mem_insight.txt', 'w', encoding='utf-8') as f: f.write('')
-    with open('sys_prompt.txt', 'r', encoding='utf-8') as f:
+    with open('sys_prompt.txt', 'r', encoding='utf-8') as f: prompt = f.read()
-        prompt = f.read()
+    prompt += get_global_memory()
-    try:
-        with open('memory/global_mem_insight.txt', 'r', encoding='utf-8') as f: 
-            insight = f.read()
-        prompt += f"\n\n[Global Memory Insight]\n"
-        prompt += 'IMPORTANT PATHS: ../memory/global_mem.txt (Facts), ../memory/global_mem_insight.txt (Logic), ../ (Your Code Root).\n'
-        prompt += 'MEM_RULE: Insight is the index of Facts. Sync Insight whenever Facts change. For details, read Facts.\n'
-        prompt += "EXT: ../memory/ may contain other task-specific memories.\n"
-        prompt += insight + "\n"
-    except FileNotFoundError: pass
     return prompt
 
 if "last_goal" not in st.session_state:
     st.session_state.last_goal = ""
 
-def refine_user_goal(raw_query, last_goal):
-    """通过 LLM 提炼用户真实意图"""
-    if not last_goal:
-        return raw_query
-
-    decide_prompt = f"""
-用户之前的目标是: "{last_goal}"
-用户现在输入了: "{raw_query}"
-
-请判断：
-1. 如果用户提供补充信息、或者是接续之前的任务，请输出合并后的【最终目标】。
-2. 如果用户只是指出之前做法有错而非变更目标，那么请输出原目标不做修改。
-3. 如果用户开启了一个完全不相关的新话题，请直接输出用户现在的输入内容。
-
-请直接输出目标描述，不要包含任何多余的文字、解释或标点。
-"""
-    try:
-        refined = llmclient.llm_func(decide_prompt).strip()
-        return refined if refined else raw_query
-    except:
-        return raw_query
-
 def agent_backend_stream(raw_query):
-    #final_goal = refine_user_goal(raw_query, st.session_state.last_goal)
-    #if final_goal != raw_query: yield f"[Goal Refined] {final_goal}\n"
-
     history = st.session_state.get("last_history", [])
-    rquery = smart_format(raw_query.replace('\n', ' '))
+    rquery = smart_format(raw_query.replace('\n', ' '), max_str_len=200)
     history.append(f"[USER]: {rquery}")
 
     sys_prompt = get_system_prompt()
@@ -85,7 +50,6 @@ def agent_backend_stream(raw_query):
     ret = yield from agent_runner_loop(llmclient,
         sys_prompt, raw_query, handler,
         TOOLS_SCHEMA, max_turns=25)
-    #st.session_state.last_goal = final_goal
     st.session_state.last_history = handler.history_info
     return ret
 

ga.py

@@ -396,13 +396,13 @@ class GenericAgentHandler(BaseHandler):
         '''Agent觉得当前任务完成后有重要信息需要记忆时调用此工具。
         目前只支持全局记忆，暂不处理过程记忆或特定任务经验。
         '''
-        next_prompt = '''### [总结提炼经验] 既然你觉得当前任务有重要信息需要记忆，请提取最近一次任务中【事实验证成功且长期有效】的环境事实与用户偏好，更新至全局记忆。
+        prompt = '''### [总结提炼经验] 既然你觉得当前任务有重要信息需要记忆，请提取最近一次任务中【事实验证成功且长期有效】的环境事实与用户偏好，更新至全局记忆。
 1. 严禁记录任何任务特定中间执行过程或临时变量经验，那是过程记忆不是全局记忆。
 2. 若无高价值新事实，那就不更新任何内容。
 3. 尽量先查看现有全局记忆形式，仅作少量修改不要影响其余部分。insight也要同步更新全局记忆的短印象来提醒存在性。
-4. 优先使用file_read和file_patch来保证少量修改。'''
+4. 优先使用file_read和file_patch来保证少量修改。''' + get_global_memory()
         yield "[Info] Start distilling good memory for long-term storage.\n"
-        return StepOutcome({"status": "success"}, next_prompt=next_prompt)
+        return StepOutcome({"status": "success"}, next_prompt=prompt)
 
     def _get_anchor_prompt(self):
         h_str = "\n".join(self.history_info[-20:])
@@ -411,3 +411,15 @@ class GenericAgentHandler(BaseHandler):
         if self.plan: prompt += f"\n<plan>{self.plan}</plan>"
         if self.focus: prompt += f"\n<focus>{self.focus}</focus>"
         return prompt + "\n请继续执行下一步。"
+
+def get_global_memory():
+    prompt = "\n"
+    try:
+        with open('memory/global_mem_insight.txt', 'r', encoding='utf-8') as f: insight = f.read()
+        prompt += f"\n\n[Global Memory Insight]\n"
+        prompt += 'IMPORTANT PATHS: ../memory/global_mem.txt (Facts), ../memory/global_mem_insight.txt (Logic), ../ (Your Code Root).\n'
+        prompt += 'MEM_RULE: Insight is the index of Facts. Sync Insight whenever Facts change. For details, read Facts.\n'
+        prompt += "EXT: ../memory/ may contain other task-specific memories.\n"
+        prompt += insight + "\n"
+    except FileNotFoundError: pass
+    return prompt

sidercall.py

@@ -11,42 +11,66 @@ class SiderLLMSession:
     def __init__(self, multiturns=6):
         self._core = Session(cookie=sider_cookie, proxies={'https':'127.0.0.1:2082'})   
     def ask(self, prompt, model="gemini-3.0-flash"):
-        if len(prompt) > 30000: prompt = prompt[-29500:]
+        if len(prompt) > 29000: 
+            print(f"[Warn] Prompt too long ({len(prompt)} chars), truncating.")
+            prompt = prompt[-29000:]
         return ''.join(self._core.chat(prompt, model))
   
 class LLMSession:
-    def __init__(self, api_key=capikey, api_base="http://113.45.39.247:3001/v1", multiturns=6):
+    def __init__(self, api_key=capikey, api_base="http://113.45.39.247:3001/v1", multiturns=6, context_win=32000):
         self.api_key = api_key
         self.api_base = api_base
+        self.raw_msgs = []
         self.messages = []
-        self.multiturns = multiturns
+        self.context_win = context_win
 
-    def ask(self, prompt, model="openai/gpt-5.1"):
+    def raw_ask(self, messages, model, temperature=0.5):
-        self.messages.append({"role": "user", "content": prompt})
+        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
-        if len(self.messages) > self.multiturns:
-            self.messages = self.messages[-self.multiturns:]
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json"
-        }
         try:
             response = requests.post(
-                f"{self.api_base}/chat/completions",
+                f"{self.api_base}/chat/completions", headers=headers, timeout=60,
-                headers=headers,
+                json={"model": model, "messages": messages, "temperature": temperature} )
-                json={
-                    "model": model, 
-                    "messages": self.messages, 
-                    "temperature": 0.5
-                },
-                timeout=60
-            )
             res_json = response.json()
             content = res_json["choices"][0]["message"]["content"]
-            self.messages.append({"role": "assistant", "content": content})
             return content
         except Exception as e:
             return f"Error: {str(e)}"
         
+    def make_messages(self, raw_list, omit_images=True):
+        messages = []
+        for msg in raw_list:
+            if omit_images and msg['image']:
+                messages.append({"role": msg['role'], "content": "[Image omitted, if you needed it, ask me]\n" + msg['prompt']})
+            elif not omit_images and msg['image']:
+                messages.append({"role": msg['role'], "content": [
+                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{msg['image']}"}},
+                    {"type": "text", "text": msg['prompt']}   ]})
+            else:
+                messages.append({"role": msg['role'], "content": msg['prompt']})
+        return messages
+       
+    def summary_history(self, model):
+        keep = max(2, len(self.raw_msgs)//2)
+        old, self.raw_msgs = self.raw_msgs[:-keep], self.raw_msgs[-keep:]
+        if len(old) == 0: old = self.raw_msgs; self.raw_msgs = []
+        p = "Summarize prev summary and prev conversations into compact memory (facts/decisions/constraints/open questions). Do NOT restate long schemas. The new summary should less than 1000 tokens.\n"
+        messages = self.make_messages(old, omit_images=True)
+        messages += [{"role":"user", "content":p}]
+        self.summary = self.raw_ask(messages, model, temperature=0.1)
+        self.raw_msgs.insert(0, {"role":"system", "prompt":"Prev summary:\n"+self.summary, "image":None})
+
+    def ask(self, prompt, model="openai/gpt-5.1", image_base64=None):
+        self.raw_msgs.append({"role": "user", "prompt": prompt, "image": image_base64})
+        messages = self.make_messages(self.raw_msgs[:-1], omit_images=True)
+        messages += self.make_messages([self.raw_msgs[-1]], omit_images=False)
+        total_len = sum(2000 if isinstance(m["content"], list) else len(str(m["content"]))//4 for m in messages)   # estimate token count
+        content = self.raw_ask(messages, model)
+        if not content.startswith("Error:"):
+            self.raw_msgs.append({"role": "assistant", "prompt": content, "image": None})
+        if total_len > self.context_win: self.summary_history(model)
+        return content
+        
+
 class MockFunction:
     def __init__(self, name, arguments):
         self.name = name

simphtml.py

@@ -1,6 +1,6 @@
 from bs4 import BeautifulSoup
 
-js_optHTML = '''function optHTML() {
+js_optHTML = r'''function optHTML() {
 function createEnhancedDOMCopy() {  
   const nodeInfo = new WeakMap();  
   const ignoreTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'META', 'LINK', 'COLGROUP', 'COL', 'TEMPLATE', 'PARAM', 'SOURCE'];  
@@ -237,7 +237,7 @@ optHTML()'''
 
 
 
-js_findMainList = '''function findMainList(startElement = null) {
+js_findMainList = r'''function findMainList(startElement = null) {
         const containerElement = startElement || document.body;  
         const rect = containerElement.getBoundingClientRect();  
         const centerX = startElement ? (rect.left + rect.width/2) : (window.innerWidth/2);