diff --git a/src/memos/api/handlers/formatters_handler.py b/src/memos/api/handlers/formatters_handler.py
index 94988295b..f9e2022a9 100644
--- a/src/memos/api/handlers/formatters_handler.py
+++ b/src/memos/api/handlers/formatters_handler.py
@@ -84,6 +84,7 @@ def post_process_pref_mem(
             {
                 "cube_id": mem_cube_id,
                 "memories": pref_formatted_mem,
+                "total_nodes": len(pref_formatted_mem),
             }
         )
         pref_instruction, pref_note = instruct_completion(pref_formatted_mem)
@@ -116,12 +117,14 @@ def post_process_textual_mem(
         {
             "cube_id": mem_cube_id,
             "memories": fact_mem,
+            "total_nodes": len(fact_mem),
         }
     )
     memories_result["tool_mem"].append(
         {
             "cube_id": mem_cube_id,
             "memories": tool_mem,
+            "total_nodes": len(tool_mem),
         }
     )
     return memories_result
diff --git a/src/memos/api/handlers/memory_handler.py b/src/memos/api/handlers/memory_handler.py
index f6f1402fc..d81d1aba2 100644
--- a/src/memos/api/handlers/memory_handler.py
+++ b/src/memos/api/handlers/memory_handler.py
@@ -6,7 +6,11 @@
 
 from typing import TYPE_CHECKING, Any, Literal
 
-from memos.api.handlers.formatters_handler import format_memory_item
+from memos.api.handlers.formatters_handler import (
+    format_memory_item,
+    post_process_pref_mem,
+    post_process_textual_mem,
+)
 from memos.api.product_models import (
     DeleteMemoryRequest,
     DeleteMemoryResponse,
@@ -209,54 +213,67 @@ def handle_get_memory(memory_id: str, naive_mem_cube: NaiveMemCube) -> GetMemory
 def handle_get_memories(
     get_mem_req: GetMemoryRequest, naive_mem_cube: NaiveMemCube
 ) -> GetMemoryResponse:
-    # TODO: Implement get memory with filter
+    results: dict[str, Any] = {"text_mem": [], "pref_mem": [], "tool_mem": []}
     memories = naive_mem_cube.text_mem.get_all(
         user_name=get_mem_req.mem_cube_id,
         user_id=get_mem_req.user_id,
         page=get_mem_req.page,
         page_size=get_mem_req.page_size,
-    )
-    total_nodes = memories["total_nodes"]
-    total_edges = memories["total_edges"]
-    del memories["total_nodes"]
-    del memories["total_edges"]
+        filter=get_mem_req.filter,
+    )["nodes"]
+
+    results = post_process_textual_mem(results, memories, get_mem_req.mem_cube_id)
+
+    if not get_mem_req.include_tool_memory:
+        results["tool_mem"] = []
 
     preferences: list[TextualMemoryItem] = []
-    total_pref = 0
 
+    format_preferences = []
     if get_mem_req.include_preference and naive_mem_cube.pref_mem is not None:
         filter_params: dict[str, Any] = {}
         if get_mem_req.user_id is not None:
             filter_params["user_id"] = get_mem_req.user_id
         if get_mem_req.mem_cube_id is not None:
             filter_params["mem_cube_id"] = get_mem_req.mem_cube_id
-
-        preferences, total_pref = naive_mem_cube.pref_mem.get_memory_by_filter(
+        if get_mem_req.filter is not None:
+            # Check and remove user_id/mem_cube_id from filter if present
+            filter_copy = get_mem_req.filter.copy()
+            removed_fields = []
+
+            if "user_id" in filter_copy:
+                filter_copy.pop("user_id")
+                removed_fields.append("user_id")
+            if "mem_cube_id" in filter_copy:
+                filter_copy.pop("mem_cube_id")
+                removed_fields.append("mem_cube_id")
+
+            if removed_fields:
+                logger.warning(
+                    f"Fields {removed_fields} found in filter will be ignored. "
+                    f"Use request-level user_id/mem_cube_id parameters instead."
+                )
+
+            filter_params.update(filter_copy)
+
+        preferences, _ = naive_mem_cube.pref_mem.get_memory_by_filter(
             filter_params, page=get_mem_req.page, page_size=get_mem_req.page_size
         )
         format_preferences = [format_memory_item(item) for item in preferences]
 
-    return GetMemoryResponse(
-        message="Memories retrieved successfully",
-        data={
-            "text_mem": [
-                {
-                    "cube_id": get_mem_req.mem_cube_id,
-                    "memories": memories,
-                    "total_nodes": total_nodes,
-                    "total_edges": total_edges,
-                }
-            ],
-            "pref_mem": [
-                {
-                    "cube_id": get_mem_req.mem_cube_id,
-                    "memories": format_preferences,
-                    "total_nodes": total_pref,
-                }
-            ],
-        },
+    results = post_process_pref_mem(
+        results, format_preferences, get_mem_req.mem_cube_id, get_mem_req.include_preference
     )
 
+    # Filter to only keep text_mem, pref_mem, tool_mem
+    filtered_results = {
+        "text_mem": results.get("text_mem", []),
+        "pref_mem": results.get("pref_mem", []),
+        "tool_mem": results.get("tool_mem", []),
+    }
+
+    return GetMemoryResponse(message="Memories retrieved successfully", data=filtered_results)
+
 
 def handle_delete_memories(delete_mem_req: DeleteMemoryRequest, naive_mem_cube: NaiveMemCube):
     logger.info(
diff --git a/src/memos/api/product_models.py b/src/memos/api/product_models.py
index d5f301c9d..b2f8a9fa3 100644
--- a/src/memos/api/product_models.py
+++ b/src/memos/api/product_models.py
@@ -771,7 +771,9 @@ class GetMemoryRequest(BaseRequest):
 
     mem_cube_id: str = Field(..., description="Cube ID")
     user_id: str | None = Field(None, description="User ID")
-    include_preference: bool = Field(True, description="Whether to handle preference memory")
+    include_preference: bool = Field(True, description="Whether to return preference memory")
+    include_tool_memory: bool = Field(False, description="Whether to return tool memory")
+    filter: dict[str, Any] | None = Field(None, description="Filter for the memory")
     page: int | None = Field(
         None,
         description="Page number (starts from 1). If None, exports all data without pagination.",
diff --git a/src/memos/mem_reader/multi_modal_struct.py b/src/memos/mem_reader/multi_modal_struct.py
index 2ed1af53e..3bf6d4927 100644
--- a/src/memos/mem_reader/multi_modal_struct.py
+++ b/src/memos/mem_reader/multi_modal_struct.py
@@ -1,5 +1,6 @@
 import concurrent.futures
 import json
+import re
 import traceback
 
 from typing import Any
@@ -547,7 +548,11 @@ def _process_tool_trajectory_fine(
         for fast_item in fast_memory_items:
             # Extract memory text (string content)
             mem_str = fast_item.memory or ""
-            if not mem_str.strip() or "tool:" not in mem_str:
+            if not mem_str.strip() or (
+                "tool:" not in mem_str
+                and "[tool_calls]:" not in mem_str
+                and not re.search(r"<tool_schema>.*?</tool_schema>", mem_str, re.DOTALL)
+            ):
                 continue
             try:
                 resp = self._get_llm_tool_trajectory_response(mem_str)
@@ -563,6 +568,8 @@ def _process_tool_trajectory_fine(
                         value=m.get("trajectory", ""),
                         info=info,
                         memory_type=memory_type,
+                        correctness=m.get("correctness", ""),
+                        experience=m.get("experience", ""),
                         tool_used_status=m.get("tool_used_status", []),
                     )
                     fine_memory_items.append(node)
@@ -606,16 +613,22 @@ def _process_multi_modal_data(
         if mode == "fast":
             return fast_memory_items
         else:
-            # Part A: call llm
+            # Part A: call llm in parallel using thread pool
             fine_memory_items = []
-            fine_memory_items_string_parser = self._process_string_fine(
-                fast_memory_items, info, custom_tags
-            )
-            fine_memory_items.extend(fine_memory_items_string_parser)
 
-            fine_memory_items_tool_trajectory_parser = self._process_tool_trajectory_fine(
-                fast_memory_items, info
-            )
+            with ContextThreadPoolExecutor(max_workers=2) as executor:
+                future_string = executor.submit(
+                    self._process_string_fine, fast_memory_items, info, custom_tags
+                )
+                future_tool = executor.submit(
+                    self._process_tool_trajectory_fine, fast_memory_items, info
+                )
+
+                # Collect results
+                fine_memory_items_string_parser = future_string.result()
+                fine_memory_items_tool_trajectory_parser = future_tool.result()
+
+            fine_memory_items.extend(fine_memory_items_string_parser)
             fine_memory_items.extend(fine_memory_items_tool_trajectory_parser)
 
             # Part B: get fine multimodal items
@@ -658,13 +671,18 @@ def _process_transfer_multi_modal_data(
         }
 
         fine_memory_items = []
-        # Part A: call llm
-        fine_memory_items_string_parser = self._process_string_fine([raw_node], info, custom_tags)
-        fine_memory_items.extend(fine_memory_items_string_parser)
+        # Part A: call llm in parallel using thread pool
+        with ContextThreadPoolExecutor(max_workers=2) as executor:
+            future_string = executor.submit(
+                self._process_string_fine, [raw_node], info, custom_tags
+            )
+            future_tool = executor.submit(self._process_tool_trajectory_fine, [raw_node], info)
 
-        fine_memory_items_tool_trajectory_parser = self._process_tool_trajectory_fine(
-            [raw_node], info
-        )
+            # Collect results
+            fine_memory_items_string_parser = future_string.result()
+            fine_memory_items_tool_trajectory_parser = future_tool.result()
+
+        fine_memory_items.extend(fine_memory_items_string_parser)
         fine_memory_items.extend(fine_memory_items_tool_trajectory_parser)
 
         # Part B: get fine multimodal items
diff --git a/src/memos/mem_reader/read_multi_modal/system_parser.py b/src/memos/mem_reader/read_multi_modal/system_parser.py
index deb2a9832..03a49afd8 100644
--- a/src/memos/mem_reader/read_multi_modal/system_parser.py
+++ b/src/memos/mem_reader/read_multi_modal/system_parser.py
@@ -1,6 +1,7 @@
 """Parser for system messages."""
 
 import ast
+import hashlib
 import json
 import re
 import uuid
@@ -42,9 +43,10 @@ def create_source(
         info: dict[str, Any],
     ) -> SourceMessage:
         """Create SourceMessage from system message."""
-        content = message["content"]
+
+        content = message.get("content", "")
         if isinstance(content, dict):
-            content = content["text"]
+            content = content.get("text", "")
 
         content_wo_tool_schema = re.sub(
             r"<tool_schema>(.*?)</tool_schema>",
@@ -84,17 +86,154 @@ def parse_fast(
         info: dict[str, Any],
         **kwargs,
     ) -> list[TextualMemoryItem]:
-        content = message["content"]
+        content = message.get("content", "")
         if isinstance(content, dict):
-            content = content["text"]
+            content = content.get("text", "")
 
-        # Replace tool_schema content with "omitted" in remaining content
-        content_wo_tool_schema = re.sub(
-            r"<tool_schema>(.*?)</tool_schema>",
-            r"<tool_schema>omitted</tool_schema>",
-            content,
-            flags=re.DOTALL,
-        )
+        # Find first tool_schema block
+        tool_schema_pattern = r"<tool_schema>(.*?)</tool_schema>"
+        match = re.search(tool_schema_pattern, content, flags=re.DOTALL)
+
+        if match:
+            original_text = match.group(0)  # Complete <tool_schema>...</tool_schema> block
+            schema_content = match.group(1)  # Content between the tags
+
+            # Parse tool schema
+            try:
+                tool_schema = json.loads(schema_content)
+                assert isinstance(tool_schema, list), "Tool schema must be a list[dict]"
+            except json.JSONDecodeError:
+                try:
+                    tool_schema = ast.literal_eval(schema_content)
+                    assert isinstance(tool_schema, list), "Tool schema must be a list[dict]"
+                except (ValueError, SyntaxError, AssertionError):
+                    logger.warning(
+                        f"[SystemParser] Failed to parse tool schema with both JSON and ast.literal_eval: {schema_content[:100]}..."
+                    )
+                    tool_schema = None
+            except AssertionError:
+                logger.warning(
+                    f"[SystemParser] Tool schema must be a list[dict]: {schema_content[:100]}..."
+                )
+                tool_schema = None
+
+            # Process and replace
+            if tool_schema is not None:
+
+                def remove_descriptions(obj):
+                    """Recursively remove all 'description' keys from a nested dict/list structure."""
+                    if isinstance(obj, dict):
+                        return {
+                            k: remove_descriptions(v) for k, v in obj.items() if k != "description"
+                        }
+                    elif isinstance(obj, list):
+                        return [remove_descriptions(item) for item in obj]
+                    else:
+                        return obj
+
+                def keep_first_layer_params(obj):
+                    """Only keep first layer parameter information, remove nested parameters."""
+                    if isinstance(obj, list):
+                        return [keep_first_layer_params(item) for item in obj]
+                    elif isinstance(obj, dict):
+                        result = {}
+                        for k, v in obj.items():
+                            if k == "properties" and isinstance(v, dict):
+                                # For properties, only keep first layer parameter names and types
+                                first_layer_props = {}
+                                for param_name, param_info in v.items():
+                                    if isinstance(param_info, dict):
+                                        # Only keep type and basic info, remove nested properties
+                                        first_layer_props[param_name] = {
+                                            key: val
+                                            for key, val in param_info.items()
+                                            if key in ["type", "enum", "required"]
+                                            and key != "properties"
+                                        }
+                                    else:
+                                        first_layer_props[param_name] = param_info
+                                result[k] = first_layer_props
+                            elif k == "parameters" and isinstance(v, dict):
+                                # Process parameters object but only keep first layer
+                                result[k] = keep_first_layer_params(v)
+                            elif isinstance(v, dict | list) and k != "properties":
+                                result[k] = keep_first_layer_params(v)
+                            else:
+                                result[k] = v
+                        return result
+                    else:
+                        return obj
+
+                def format_tool_schema_readable(tool_schema):
+                    """Convert tool schema to readable format: tool_name: [param1 (type1), ...](required: ...)"""
+                    lines = []
+                    for tool in tool_schema:
+                        if not tool:
+                            continue
+
+                        # Handle both new format and old-style OpenAI function format
+                        if tool.get("type") == "function" and "function" in tool:
+                            tool_info = tool.get("function")
+                            if not tool_info:
+                                continue
+                        else:
+                            tool_info = tool
+
+                        tool_name = tool_info.get("name", "unknown")
+                        params_obj = tool_info.get("parameters", {})
+                        properties = params_obj.get("properties", {})
+                        required = params_obj.get("required", [])
+
+                        # Format parameters
+                        param_strs = []
+                        for param_name, param_info in properties.items():
+                            if isinstance(param_info, dict):
+                                param_type = param_info.get("type", "any")
+                                # Handle enum
+                                if "enum" in param_info and param_info["enum"] is not None:
+                                    # Ensure all enum values are strings
+                                    enum_values = [str(v) for v in param_info["enum"]]
+                                    param_type = f"{param_type}[{', '.join(enum_values)}]"
+                                param_strs.append(f"{param_name} ({param_type})")
+                            else:
+                                param_strs.append(f"{param_name} (any)")
+
+                        # Format required parameters
+                        # Ensure all required parameter names are strings
+                        required_strs = [str(r) for r in required] if required else []
+                        required_str = (
+                            f"(required: {', '.join(required_strs)})" if required_strs else ""
+                        )
+
+                        # Construct the line
+                        params_part = f"[{', '.join(param_strs)}]" if param_strs else "[]"
+                        line = f"{tool_name}: {params_part}{required_str}"
+                        lines.append(line)
+
+                    return "\n".join(lines)
+
+                # Compression mode literal: ["compress", "omit"]. compress is core-information-preserving, omit is full omission.
+                compression_mode = "compress"
+                if compression_mode == "omit":
+                    processed_text = "<tool_schema>omitted</tool_schema>"
+                elif compression_mode == "compress":
+                    # First keep only first layer params, then remove descriptions
+                    simple_tool_schema = keep_first_layer_params(tool_schema)
+                    simple_tool_schema = remove_descriptions(simple_tool_schema)
+                    # change to readable format
+                    readable_schema = format_tool_schema_readable(simple_tool_schema)
+
+                    processed_text = f"<tool_schema>{readable_schema}</tool_schema>"
+                else:
+                    raise ValueError(f"Unknown compression mode: {compression_mode}")
+
+                content = content.replace(original_text, processed_text, 1)
+
+        parts = ["system: "]
+        if message.get("chat_time"):
+            parts.append(f"[{message.get('chat_time')}]: ")
+        prefix = "".join(parts)
+        msg_line = f"{prefix}{content}\n"
 
         source = self.create_source(message, info)
 
@@ -104,7 +243,7 @@ def parse_fast(
         session_id = info_.pop("session_id", "")
 
         # Split parsed text into chunks
-        content_chunks = self._split_text(content_wo_tool_schema)
+        content_chunks = self._split_text(msg_line)
 
         memory_items = []
         for _chunk_idx, chunk_text in enumerate(content_chunks):
@@ -132,9 +271,9 @@ def parse_fine(
         info: dict[str, Any],
         **kwargs,
     ) -> list[TextualMemoryItem]:
-        content = message["content"]
+        content = message.get("content", "")
         if isinstance(content, dict):
-            content = content["text"]
+            content = content.get("text", "")
         try:
             tool_schema = json.loads(content)
             assert isinstance(tool_schema, list), "Tool schema must be a list[dict]"
@@ -155,6 +294,22 @@ def parse_fine(
         user_id = info_.pop("user_id", "")
         session_id = info_.pop("session_id", "")
 
+        # Deduplicate tool schemas based on memory content
+        # Use hash as key for efficiency, but store original string to handle collisions
+        seen_memories = {}  # hash -> memory_str mapping
+        unique_schemas = []
+        for schema in tool_schema:
+            memory_str = json.dumps(schema, ensure_ascii=False, sort_keys=True)
+            # Use SHA-256 for better collision resistance
+            memory_hash = hashlib.sha256(memory_str.encode("utf-8")).hexdigest()
+
+            # Check if hash exists and verify the actual content (handle potential collision)
+            if memory_hash not in seen_memories:
+                seen_memories[memory_hash] = memory_str
+                unique_schemas.append(schema)
+            elif seen_memories[memory_hash] != memory_str:
+                unique_schemas.append(schema)
+
         return [
             TextualMemoryItem(
                 id=str(uuid.uuid4()),
@@ -168,5 +323,5 @@ def parse_fine(
                     info=info_,
                 ),
             )
-            for schema in tool_schema
+            for schema in unique_schemas
         ]
diff --git a/src/memos/memories/textual/tree.py b/src/memos/memories/textual/tree.py
index c486e6cf6..b963cfa9b 100644
--- a/src/memos/memories/textual/tree.py
+++ b/src/memos/memories/textual/tree.py
@@ -327,13 +327,14 @@ def get_all(
         user_id: str | None = None,
         page: int | None = None,
         page_size: int | None = None,
+        filter: dict | None = None,
     ) -> dict:
         """Get all memories.
         Returns:
             list[TextualMemoryItem]: List of all memories.
         """
         graph_output = self.graph_store.export_graph(
-            user_name=user_name, user_id=user_id, page=page, page_size=page_size
+            user_name=user_name, user_id=user_id, page=page, page_size=page_size, filter=filter
         )
         return graph_output
 
diff --git a/src/memos/templates/tool_mem_prompts.py b/src/memos/templates/tool_mem_prompts.py
index 7d5363956..2fe8840b7 100644
--- a/src/memos/templates/tool_mem_prompts.py
+++ b/src/memos/templates/tool_mem_prompts.py
@@ -1,26 +1,47 @@
 TOOL_TRAJECTORY_PROMPT_ZH = """
-你是一个专业的工具调用轨迹提取专家。你的任务是从给定的对话消息中提取完整的工具调用轨迹经验。
+你是一个专业的工具经验提取专家。你的任务是从给定的对话消息中提取完整的工具调用轨迹经验。
 
-## 提取规则：
-1. 只有当对话中存在有价值的工具调用过程时才进行提取
-2. 有价值的轨迹至少包含以下元素：
-   - 用户的问题（user message）
-   - 助手的工具调用尝试（assistant message with tool_calls）
-   - 工具的执行结果（tool message with tool_call_id and content，无论成功或失败）
-   - 助手的响应（assistant message，无论是否给出最终答案）
+## 分析判断步骤：
+**步骤1：判断任务完成度**
+根据用户反馈，判定correctness：success（成功）或 failed（失败），用户反馈决定权大于执行结果，用户反馈有误，则判定为failed
+
+**步骤2：成功轨迹（success）- 经验提炼**
+从成功模式中提炼通用原则或规则，采用"when...then..."结构：
+- when: 明确描述触发该经验的场景特征（任务类型、工具环境、参数特征等）
+- then: 总结有效的参数模式、调用策略、最佳实践
+注意：经验是解决整个轨迹问题级别的，不仅仅针对单个工具
+
+**步骤3：失败轨迹（failed）- 错误分析与经验提炼**
+3.1 工具需求判断
+  - 任务是否需要工具？（需要/直接回答/误调用）
+3.2 工具调用检查
+  - 工具存在性：是否在system中提供
+  - 工具选择：是否选对工具
+  - 参数正确性：是否符合类型定义
+  - 幻觉检测：是否调用不存在的工具
+3.3 错误根因定位
+  结合消息中的错误反馈信息和上述分析，精准输出根本原因
+3.4 经验提炼（核心）
+  从失败模式中提炼通用原则或规则，采用"when...then..."结构：
+  - when: 明确描述触发该经验的场景特征（任务类型、工具环境、参数特征等）
+  - then: 给出避免错误的通用策略、正确调用方式或决策规则
+  注意：经验是解决整个轨迹问题级别的，不仅仅针对单个工具
 
 ## 输出格式：
 返回一个JSON数组，格式如下：
+
 ```json
 [
   {
-    "trajectory": "自然语言输出包含'任务、使用的工具、工具观察、最终回答'的完整精炼的总结，体现顺序",
+    "correctness": "success 或 failed",
+    "trajectory": "精炼完整的自然语言总结，包含：[任务（用户任务） -> 执行动作（调用的工具/直接回答） -> 执行结果] (可能多轮) -> 最终回答",
+    "experience": "采用when...then...格式，例如：'when 遇到XX的任务时，应该YY'",
     "tool_used_status": [
       {
-        "used_tool": "工具名1",
+        "used_tool": "工具名称（如果调用了工具）",
         "success_rate": "0.0-1.0之间的数值，表示该工具在本次轨迹中的成功率",
         "error_type": "调用失败时的错误类型和描述，成功时为空字符串",
-        "experience": "该工具的使用经验，比如常见的参数模式、执行特点、结果解读方式等"
+        "tool_experience": "调用该工具的经验，包括可能的前置条件和可能的后置效果"
       }
     ]
   }
@@ -28,42 +49,72 @@
 ```
 
 ## 注意事项：
-- 如果对话中没有完整的工具调用轨迹，返回空数组
 - 每个轨迹必须是独立的完整过程
 - 一个轨迹中可能涉及多个工具的使用，每个工具在tool_used_status中独立记录
+- 如果没有调用工具，tool_used_status为空数组[]
+- 如果多条轨迹存在顺序依赖关系，需要将它们视为一条轨迹
 - 只提取事实内容，不要添加任何解释或额外信息
 - 确保返回的是有效的JSON格式
+- 输出的trajectory需要按照messages的发展顺序排列
+- experience必须是通用的、可复用的经验规则，而不是针对具体案例的描述
+- 无论成功或失败，都要提炼经验并使用when...then...格式
 
-请分析以下对话消息并提取工具调用轨迹：
-
+请分析以下对话消息并提取工具调用轨迹，基于以下对话消息：
+<messages>
 {messages}
-
+</messages>
 """
 
 
 TOOL_TRAJECTORY_PROMPT_EN = """
-You are a professional tool call trajectory extraction expert. Your task is to extract valuable tool call trajectory experiences from given conversation messages.
+You are a professional tool experience extraction expert. Your task is to extract complete tool call trajectory experiences from given conversation messages.
+
+## Analysis and Judgment Steps:
+
+**Step 1: Assess Task Completion**
+Determine correctness based on user feedback: success or failed, user feedback has higher priority than execution results, if user feedback is incorrect, then determine as failed
+
+**Step 2: Successful Trajectory (success) - Experience Extraction**
+Extract general principles or rules from success patterns, using "when...then..." structure:
+- when: clearly describe the scenario characteristics that trigger this experience (task type, tool environment, parameter characteristics, etc.)
+- then: summarize effective parameter patterns, calling strategies, and best practices
+Note: Experience is at the trajectory-level problem-solving, not just for a single tool
 
-## Extraction Rules:
-1. Only extract when there are valuable tool calling processes in the conversation
-2. Valuable trajectories must contain at least the following elements:
-   - User's question (user message)
-   - Assistant's tool call attempt (assistant message with tool_calls)
-   - Tool execution results (tool message with tool_call_id and content, regardless of success or failure)
-   - Assistant's response (assistant message, whether or not a final answer is given)
+**Step 3: Failed Trajectory (failed) - Error Analysis and Experience Extraction**
+
+3.1 Tool Requirement Assessment
+  - Does the task require tools? (required/direct answer/unnecessary call)
+
+3.2 Tool Call Verification
+  - Tool availability: provided in system?
+  - Tool selection: correct tool chosen?
+  - Parameter correctness: conform to type definitions?
+  - Hallucination detection: calling non-existent tools?
+
+3.3 Root Cause Identification
+  Combine error feedback from messages with above analysis to precisely output root cause
+
+3.4 Experience Extraction (Core)
+  Extract general principles or rules from failure patterns, using "when...then..." structure:
+  - when: clearly describe the scenario characteristics that trigger this experience (task type, tool environment, parameter characteristics, etc.)
+  - then: provide general strategies to avoid errors, correct calling approaches, or decision rules
+  Note: Experience is at the trajectory-level problem-solving, not just for a single tool
 
 ## Output Format:
 Return a JSON array in the following format:
+
 ```json
 [
   {
-    "trajectory": "Natural language summary containing 'task, tools used, tool observations, final answer' in a complete and refined manner, reflecting the sequence",
+    "correctness": "success or failed",
+    "trajectory": "Concise and complete natural language summary including: [task (user task) -> execution action (tool called/direct answer) -> execution result] (possibly multiple rounds) -> final answer",
+    "experience": "Use when...then... format, e.g., 'when encountering XX tasks, should do YY'",
     "tool_used_status": [
       {
-        "used_tool": "Tool Name 1",
-        "success_rate": "Numerical value between 0.0-1.0, indicating the success rate of this tool in the current trajectory",
+        "used_tool": "Tool name (if tool was called)",
+        "success_rate": "Numerical value between 0.0-1.0, indicating the success rate of this tool in current trajectory",
         "error_type": "Error type and description when call fails, empty string when successful",
-        "experience": "Usage experience of this tool, such as common parameter patterns, execution characteristics, result interpretation methods, etc."
+        "tool_experience": "Experience of using this tool, including possible preconditions and possible post-effects"
       }
     ]
   }
@@ -71,14 +122,18 @@
 ```
 
 ## Notes:
-- If there are no complete tool call trajectories in the conversation, return an empty array
 - Each trajectory must be an independent complete process
-- Multiple tools may be used in one trajectory, each tool is recorded independently in tool_used_status
-- Only extract factual content, do not add any additional explanations or information
+- A trajectory may involve multiple tools, each recorded independently in tool_used_status
+- If no tool was called, tool_used_status is an empty array []
+- If multiple trajectories have sequential dependencies, treat them as one trajectory
+- Only extract factual content, do not add any explanations or extra information
 - Ensure the returned content is valid JSON format
+- The trajectory should be arranged according to the development order of messages
+- Experience must be general and reusable rules, not descriptions specific to concrete cases
+- Whether success or failed, always extract experience using when...then... format
 
-Please analyze the following conversation messages and extract tool call trajectories:
-
+Please analyze the following conversation messages and extract tool call trajectories based on:
+<messages>
 {messages}
-
+</messages>
 """