ai-agent-book/chapter2/kv-cache/result_sliding_window_20260718_kimi_k2_6.json

{
  "success": false,
  "iterations": 5,
  "tool_calls": [
    "ToolCall(name='find', arguments={'pattern': '*.py'}, result={'pattern': '*.py', 'directory': '.', 'matches': ['agent.py', 'demo_quick.py', 'main.py', 'test_api.py', 'test_cache_invalidation.py', 'test_cached_tokens.py', 'test_completion.py', 'test_error_handling.py', 'test_file_range.py', 'test_interactive.py', 'test_message_flow.py', 'test_tools.py', 'test_ttft.py'], 'count': 13, 'truncated': False, 'success': True}, error=None, timestamp=1784337602.4177501)",
    "ToolCall(name='find', arguments={'pattern': '*.py'}, result={'pattern': '*.py', 'directory': '.', 'matches': ['agent.py', 'demo_quick.py', 'main.py', 'test_api.py', 'test_cache_invalidation.py', 'test_cached_tokens.py', 'test_completion.py', 'test_error_handling.py', 'test_file_range.py', 'test_interactive.py', 'test_message_flow.py', 'test_tools.py', 'test_ttft.py'], 'count': 13, 'truncated': False, 'success': True}, error=None, timestamp=1784337604.403392)",
    "ToolCall(name='find', arguments={'pattern': '*.py'}, result={'pattern': '*.py', 'directory': '.', 'matches': ['agent.py', 'demo_quick.py', 'main.py', 'test_api.py', 'test_cache_invalidation.py', 'test_cached_tokens.py', 'test_completion.py', 'test_error_handling.py', 'test_file_range.py', 'test_interactive.py', 'test_message_flow.py', 'test_tools.py', 'test_ttft.py'], 'count': 13, 'truncated': False, 'success': True}, error=None, timestamp=1784337608.362566)",
    "ToolCall(name='read_file', arguments={'file_path': 'main.py'}, result={'path': 'main.py', 'content': '\"\"\"\\nMain script to demonstrate KV cache importance\\nRuns the ReAct agent with different implementations and compares performance\\n\"\"\"\\n\\nimport os\\nimport sys\\nimport glob\\nimport json\\nimport argparse\\nimport logging\\nfrom typing import Dict, List, Any\\nfrom datetime import datetime\\nfrom dataclasses import asdict\\nfrom agent import KVCacheAgent, KVCacheMode, AgentMetrics, compare_implementations\\n\\n# Default model (Moonshot / Kimi). The whole current Kimi family (k2.5/k2.6/\\n# k2.7/k3) reports cached_tokens for automatic prefix caching AND reasons, so it\\n# only accepts temperature=1 (agent.py handles that automatically). kimi-k2.6 has\\n# the lightest reasoning footprint of the cache-reporting models, giving the\\n# cleanest TTFT while still exposing the prefix-cache hit metric this demo needs.\\n# (The non-reasoning moonshot-v1-* models do NOT report cached_tokens, so they\\n# cannot demonstrate the cache effect.)\\nDEFAULT_MODEL = \"kimi-k2.6\"\\n\\n# Configure logging\\nlogging.basicConfig(\\n    level=logging.INFO,\\n    format=\\'%(asctime)s - %(levelname)s - %(message)s\\',\\n    handlers=[\\n        logging.FileHandler(\\'kv_cache_demo.log\\'),\\n        logging.StreamHandler()\\n    ]\\n)\\nlogger = logging.getLogger(__name__)\\n\\n\\n# ---------------------------------------------------------------------------\\n# Metrics helpers (shared by live comparison and offline report)\\n# ---------------------------------------------------------------------------\\n\\ndef _coerce_metrics(metrics: Any) -> Dict[str, Any]:\\n    \"\"\"Normalize a stored metrics value into a plain dict.\\n\\n    Handles both formats found in result files:\\n      - dict: produced by --compare (asdict) and by the fixed --mode path\\n      - str : legacy single-mode files that stored repr(AgentMetrics(...))\\n              because json.dump used default=str\\n    \"\"\"\\n    if isinstance(metrics, dict):\\n        return metrics\\n    if isinstance(metrics, str) and metrics.startswith(\"AgentMetrics(\"):\\n        # Safe eval: only AgentMetrics is exposed, no builtins.\\n        try:\\n            obj = eval(metrics, {\"__builtins__\": {}}, {\"AgentMetrics\": AgentMetrics})\\n            return asdict(obj)\\n        except Exception as e:  # pragma: no cover - defensive\\n            logger.warning(f\"Could not parse legacy metrics string: {e}\")\\n    return {}\\n\\n\\ndef _avg_ttft(m: Dict[str, Any]) -> float:\\n    \"\"\"Average TTFT across iterations, falling back to first-iteration TTFT.\"\"\"\\n    lst = m.get(\"ttft_per_iteration\") or []\\n    return sum(lst) / len(lst) if lst else float(m.get(\"ttft\", 0.0) or 0.0)\\n\\n\\ndef _hit_rate(m: Dict[str, Any]) -> float:\\n    total = (m.get(\"cache_hits\", 0) or 0) + (m.get(\"cache_misses\", 0) or 0)\\n    return (m.get(\"cache_hits\", 0) or 0) / total * 100 if total else 0.0\\n\\n\\ndef _billable_tokens(m: Dict[str, Any], cache_price_ratio: float) -> float:\\n    \"\"\"Illustrative billable prompt tokens under a prompt-cache discount.\\n\\n    cached tokens are charged at cache_price_ratio of the normal price; the\\n    rest at full price. This is a transparent function of the *measured*\\n    token counts and a user-supplied ratio - it is not a fabricated\\n    provider-specific price.\\n    \"\"\"\\n    prompt = m.get(\"prompt_tokens\", 0) or 0\\n    cached = m.get(\"cached_tokens\", 0) or 0\\n    cached = min(cached, prompt)\\n    return (prompt - cached) + cached * cache_price_ratio\\n\\n\\ndef print_comparison_table(results: Dict[str, Any], cache_price_ratio: float = 0.1) -> None:\\n    \"\"\"Render the cross-strategy comparison table (latency / cache / cost).\"\"\"\\n    print(f\"\\\\n{\\'Mode\\':<16} {\\'Iters\\':<6} {\\'1st TTFT\\':<10} {\\'Avg TTFT\\':<10} \"\\n          f\"{\\'Total(s)\\':<10} {\\'Prompt\\':<9} {\\'Cached\\':<9} {\\'Hit%\\':<7} \"\\n          f\"{\\'Cache%\\':<8} {\\'Bill.Tok\\':<10} {\\'Save%\\':<7}\")\\n    print(\"-\" * 112)\\n\\n    for mode, data in results.items():\\n        m = _coerce_metrics(data.get(\"metrics\", {}))\\n        prompt = m.get(\"prompt_tokens\", 0) or 0\\n        cached = m.get(\"cached_tokens\", 0) or 0\\n        iters = data.get(\"iterations\", m.get(\"iterations\", 0)) or 0\\n        cache_pct = cached / prompt * 100 if prompt else 0.0\\n        billable = _billable_tokens(m, cache_price_ratio)\\n        save_pct = (prompt - billable) / prompt * 100 if prompt else 0.0\\n\\n        print(f\"{mode:<16} {iters:<6} {float(m.get(\\'ttft\\', 0.0) or 0.0):<10.3f} \"\\n              f\"{_avg_ttft(m):<10.3f} {float(m.get(\\'total_time\\', 0.0) or 0.0):<10.3f} \"\\n              f\"{prompt:<9,} {cached:<9,} {_hit_rate(m):<7.1f} \"\\n              f\"{cache_pct:<8.1f} {billable:<10,.0f} {save_pct:<7.1f}\")\\n\\n    print(\"-\" * 112)\\n    print(f\"\u6ce8\uff1aBill.Tok / Save% \u5047\u8bbe\u7f13\u5b58 token \u6309\u6b63\u5e38\u4ef7\u7684 {cache_price_ratio:.0%} \u8ba1\u8d39\"\\n          f\"\uff08\u53ef\u7528 --cache-price-ratio \u8c03\u6574\uff09\uff0c\u4ec5\u4e3a\u6210\u672c\u793a\u610f\uff0c\u975e\u67d0\u5bb6\u670d\u52a1\u5546\u5b9e\u9645\u62a5\u4ef7\u3002\")\\n\\n\\ndef load_result_files(paths: List[str]) -> Dict[str, Any]:\\n    \"\"\"Load result_*.json files into a {mode: {...}} dict for offline reporting.\"\"\"\\n    results: Dict[str, Any] = {}\\n    for path in sorted(paths):\\n        try:\\n            with open(path, \\'r\\') as f:\\n                data = json.load(f)\\n        except Exception as e:\\n            logger.warning(f\"Skipping {path}: {e}\")\\n            continue\\n\\n        # A comparison_*.json holds many modes; a result_*.json holds one.\\n        if \"mode\" not in data and all(isinstance(v, dict) and \"metrics\" in v\\n                                      for v in data.values()):\\n            for mode, entry in data.items():\\n                results[mode] = {\"metrics\": _coerce_metrics(entry.get(\"metrics\", {})),\\n                                 \"iterations\": entry.get(\"iterations\"),\\n                                 \"_source\": path}\\n        else:\\n            mode = data.get(\"mode\", os.path.splitext(os.path.basename(path))[0])\\n            results[mode] = {\"metrics\": _coerce_metrics(data.get(\"metrics\", {})),\\n                             \"iterations\": data.get(\"iterations\"),\\n                             \"_source\": path}\\n    return results\\n\\n\\ndef run_report(inputs: List[str] = None, cache_price_ratio: float = 0.1) -> None:\\n    \"\"\"Offline: build the comparison table from existing result_*.json files.\\n\\n    No API key required - reads previously saved runs so the final result is\\n    legible in one command without re-hitting the model.\\n    \"\"\"\\n    if not inputs:\\n        inputs = [\"result_*.json\", \"comparison_*.json\"]\\n\\n    paths: List[str] = []\\n    for item in inputs:\\n        if os.path.isdir(item):\\n            paths.extend(glob.glob(os.path.join(item, \"result_*.json\")))\\n            paths.extend(glob.glob(os.path.join(item, \"comparison_*.json\")))\\n        else:\\n            paths.extend(glob.glob(item))\\n\\n    paths = sorted(set(paths))\\n    if not paths:\\n        logger.error(\"\u672a\u627e\u5230\u4efb\u4f55 result_*.json / comparison_*.json \u7ed3\u679c\u6587\u4ef6\u3002\"\\n                     \"\u8bf7\u5148\u8fd0\u884c --mode \u6216 --compare \u751f\u6210\u7ed3\u679c\uff0c\u6216\u7528 --input \u6307\u5b9a\u8def\u5f84\u3002\")\\n        sys.exit(1)\\n\\n    results = load_result_files(paths)\\n\\n    print(\"\\\\n\" + \"=\" * 112)\\n    print(\"KV CACHE \u79bb\u7ebf\u5bf9\u6bd4\u62a5\u544a\uff08\u57fa\u4e8e\u5df2\u4fdd\u5b58\u7684\u5b9e\u6d4b\u7ed3\u679c\uff09\")\\n    print(\"=\" * 112)\\n    print(f\"\u6570\u636e\u6765\u6e90\uff08{len(paths)} \u4e2a\u6587\u4ef6\uff09:\")\\n    for mode, data in results.items():\\n        print(f\"  \u2022 {mode:<16} \u2190 {os.path.basename(data.get(\\'_source\\', \\'?\\'))}\")\\n\\n    print_comparison_table(results, cache_price_ratio)\\n\\n    print(\"\\\\n\ud83d\udcdd \u8bf4\u660e\uff1a\u4e0d\u540c\u7ed3\u679c\u6587\u4ef6\u53ef\u80fd\u6765\u81ea\u4e0d\u540c\u4efb\u52a1/\u65f6\u95f4\uff0c\u7edd\u5bf9\u6570\u503c\u4ec5\u4f9b\u540c\u4e00\u6b21\u8fd0\u884c\u5185\u6a2a\u5411\u5bf9\u6bd4\uff1b\"\\n          \"\u5982\u9700\u4e25\u683c\u5bf9\u7167\uff0c\u8bf7\u7528 --compare \u5728\u540c\u4e00\u4efb\u52a1\u4e0b\u4e00\u6b21\u6027\u751f\u6210\u5168\u90e8\u6a21\u5f0f\u7684\u6570\u636e\u3002\")\\n\\n\\ndef create_summary_task() -> str:\\n    \"\"\"Create a task that requires reading multiple files\"\"\"\\n    return \"\"\"Please analyze and summarize all the projects in the week1 and week2 directories.\\nFor each project:\\n1. Find all Python files\\n2. Read the main files and understand the functionality\\n3. Identify the key features and purpose\\n4. Provide a comprehensive summary\\n\\nStart with week1 projects, then move to week2. Be thorough in your analysis.\"\"\"\\n\\n\\ndef run_single_mode(api_key: str, mode: str, task: str = None, root_dir: str = \"../..\",\\n                    model: str = DEFAULT_MODEL, output: str = None):\\n    \"\"\"\\n    Run agent in a single mode\\n\\n    Args:\\n        api_key: API key for Kimi\\n        mode: KV cache mode to use\\n        task: Custom task (optional)\\n        root_dir: Root directory for file operations (default: \"../..\" = /projects from kv-cache dir)\\n        model: Model to use\\n        output: Output path for the result JSON (optional; auto-named if omitted)\\n    \"\"\"\\n    # Parse mode\\n    mode_map = {\\n        \"correct\": KVCacheMode.CORRECT,\\n        \"dynamic_system\": KVCacheMode.DYNAMIC_SYSTEM,\\n        \"shuffled_tools\": KVCacheMode.SHUFFLED_TOOLS,\\n        \"dynamic_profile\": KVCacheMode.DYNAMIC_PROFILE,\\n        \"sliding_window\": KVCacheMode.SLIDING_WINDOW,\\n        \"text_format\": KVCacheMode.TEXT_FORMAT\\n    }\\n    \\n    if mode not in mode_map:\\n        logger.error(f\"Invalid mode: {mode}\")\\n        logger.info(f\"Valid modes: {\\', \\'.join(mode_map.keys())}\")\\n        return\\n    \\n    # Use default task if not provided\\n    if not task:\\n        task = create_summary_task()\\n    \\n    logger.info(f\"Running in mode: {mode}\")\\n    logger.info(f\"Task: {task}\")\\n    logger.info(\"=\"*80)\\n    \\n    # Create agent and execute task\\n    agent = KVCacheAgent(\\n        api_key=api_key,\\n        mode=mode_map[mode],\\n        model=model,\\n        root_dir=root_dir,\\n        verbose=True\\n    )\\n    \\n    result = agent.execute_task(task, max_iterations=30)\\n    \\n    # Print results\\n    print(\"\\\\n\" + \"=\"*80)\\n    print(f\"EXECUTION RESULTS - Mode: {mode}\")\\n    print(\"=\"*80)\\n    \\n    metrics = result[\"metrics\"]\\n    print(f\"\\\\n\ud83d\udcca Performance Metrics:\")\\n    print(f\"  \u2022 Time to First Token (TTFT): {metrics.ttft:.3f} seconds\")\\n    \\n    # Show TTFT progression\\n    if metrics.ttft_per_iteration:\\n        print(f\"  \u2022 TTFT per iteration:\")\\n        for i, ttft in enumerate(metrics.ttft_per_iteration, 1):\\n            print(f\"      Iteration {i}: {ttft:.3f}s\")\\n\\n        # Show improvement\\n        if len(metrics.ttft_per_iteration) > 1:\\n            first_ttft = metrics.ttft_per_iteration[0]\\n            last_ttft = metrics.ttft_per_iteration[-1]\\n            avg_after_first = sum(metrics.ttft_per_iteration[1:]) / len(metrics.ttft_per_iteration[1:])\\n            print(f\"  \u2022 TTFT Analysis:\")\\n            print(f\"      First iteration: {first_ttft:.3f}s\")\\n            print(f\"      Last iteration: {', 'total_lines': 537, 'lines_read': 537, 'offset': 0, 'end_line': 537, 'truncated': True, 'success': True}, error=None, timestamp=1784337614.781182)",
    "ToolCall(name='read_file', arguments={'file_path': 'agent.py'}, result={'path': 'agent.py', 'content': '\"\"\"\\nKV Cache Demonstration Agent with ReAct Pattern\\nDemonstrates the importance of KV cache through correct and incorrect implementations.\\nUses local file system tools to read and search through code files.\\n\"\"\"\\n\\nimport json\\nimport os\\nimport re\\nimport time\\nimport logging\\nimport random\\nfrom typing import List, Dict, Any, Optional, Tuple\\nfrom dataclasses import dataclass, field, asdict\\nfrom enum import Enum\\nfrom datetime import datetime\\nfrom openai import OpenAI\\nimport glob as glob_module\\nimport subprocess\\n\\n\\ndef _is_reasoning_model(model) -> bool:\\n    \"\"\"True for models that emit reasoning_content and only accept temperature=1.\\n\\n    On the live Moonshot endpoint the whole current Kimi family reasons:\\n    kimi-k2.5 / kimi-k2.6 / kimi-k2.7* / kimi-k3. The legacy moonshot-v1-*\\n    chat models do NOT reason (and also do not report cached_tokens).\"\"\"\\n    m = str(model or \"\").lower().replace(\"/\", \"-\")\\n    if \"gpt-5\" in m:\\n        return True\\n    return any(tag in m for tag in (\"kimi-k2.5\", \"kimi-k2.6\", \"kimi-k2.7\", \"kimi-k3\"))\\n\\n\\ndef _reasoning_safe_temperature(model, requested=1.0):\\n    \"\"\"Reasoning models (Kimi K2.5/K2.6/K2.7/K3, GPT-5, ...) only accept\\n    temperature=1. Return 1 for those; otherwise the requested value so\\n    non-reasoning providers (moonshot-v1, Doubao, DeepSeek) are unchanged.\"\"\"\\n    return 1 if _is_reasoning_model(model) else requested\\n\\n\\ndef _reasoning_safe_max_tokens(model, requested=2000):\\n    \"\"\"Reasoning models spend completion budget on hidden reasoning tokens\\n    before emitting content / tool calls. Give them enough headroom so a\\n    tool call is not truncated away; leave non-reasoning models unchanged.\"\"\"\\n    return max(requested, 4096) if _is_reasoning_model(model) else requested\\n\\n\\n# Configure logging\\nlogging.basicConfig(level=logging.INFO, format=\\'%(asctime)s - %(levelname)s - %(message)s\\')\\nlogger = logging.getLogger(__name__)\\n\\n\\nclass KVCacheMode(Enum):\\n    \"\"\"Different KV cache optimization modes\"\"\"\\n    CORRECT = \"correct\"  # Correct implementation with stable context\\n    DYNAMIC_SYSTEM = \"dynamic_system\"  # Changing system prompt with timestamp\\n    SHUFFLED_TOOLS = \"shuffled_tools\"  # Shuffling tool order each request\\n    DYNAMIC_PROFILE = \"dynamic_profile\"  # Changing user profile with credits\\n    SLIDING_WINDOW = \"sliding_window\"  # Only keeping recent 5 messages\\n    TEXT_FORMAT = \"text_format\"  # Formatting messages as plain text\\n\\n\\n@dataclass\\nclass ToolCall:\\n    \"\"\"Represents a single tool call\"\"\"\\n    name: str\\n    arguments: Dict[str, Any]\\n    result: Any = None\\n    error: Optional[str] = None\\n    timestamp: float = field(default_factory=time.time)\\n\\n\\n@dataclass\\nclass AgentMetrics:\\n    \"\"\"Metrics for agent performance\"\"\"\\n    ttft: float = 0.0  # Time to first token (first iteration)\\n    ttft_per_iteration: List[float] = field(default_factory=list)  # TTFT for each iteration\\n    total_time: float = 0.0\\n    iterations: int = 0\\n    tool_calls: int = 0\\n    cache_hits: int = 0\\n    cache_misses: int = 0\\n    prompt_tokens: int = 0\\n    completion_tokens: int = 0\\n    cached_tokens: int = 0\\n\\n\\nclass LocalFileTools:\\n    \"\"\"Local implementations of file system tools\"\"\"\\n    \\n    def __init__(self, root_dir: str = \".\"):\\n        self.root_dir = os.path.abspath(root_dir)\\n        logger.info(f\"File tools initialized with root: {self.root_dir}\")\\n    \\n    def read_file(self, file_path: str, offset: int = 0, size: int = None) -> Dict[str, Any]:\\n        \"\"\"\\n        Read contents of a file\\n        \\n        Args:\\n            file_path: Path to the file relative to root directory\\n            offset: Line number to start reading from (0-based, default: 0)\\n            size: Number of lines to read (default: None, read all)\\n            \\n        Returns:\\n            Dictionary with file contents or error\\n        \"\"\"\\n        try:\\n            full_path = os.path.join(self.root_dir, file_path)\\n            \\n            # Security check - ensure path is within root_dir\\n            real_path = os.path.realpath(full_path)\\n            if not real_path.startswith(self.root_dir):\\n                return {\\n                    \"error\": f\"Access denied: Path outside root directory\",\\n                    \"success\": False\\n                }\\n            \\n            with open(real_path, \\'r\\', encoding=\\'utf-8\\', errors=\\'ignore\\') as f:\\n                lines = f.readlines()\\n            \\n            total_lines = len(lines)\\n            \\n            # Apply offset and size\\n            if offset < 0:\\n                offset = 0\\n            if offset >= total_lines:\\n                return {\\n                    \"path\": file_path,\\n                    \"content\": \"\",\\n                    \"total_lines\": total_lines,\\n                    \"lines_read\": 0,\\n                    \"offset\": offset,\\n                    \"success\": True,\\n                    \"message\": f\"Offset {offset} exceeds file length ({total_lines} lines)\"\\n                }\\n            \\n            # Determine end line\\n            if size is None:\\n                end = total_lines\\n            else:\\n                end = min(offset + size, total_lines)\\n            \\n            # Get the requested lines\\n            selected_lines = lines[offset:end]\\n            content = \\'\\'.join(selected_lines)\\n            \\n            # Apply size limit for safety (10KB)\\n            truncated = False\\n            if len(content) > 10000:\\n                content = content[:10000]\\n                truncated = True\\n            \\n            return {\\n                \"path\": file_path,\\n                \"content\": content,\\n                \"total_lines\": total_lines,\\n                \"lines_read\": len(selected_lines),\\n                \"offset\": offset,\\n                \"end_line\": end,\\n                \"truncated\": truncated,\\n                \"success\": True\\n            }\\n        except FileNotFoundError:\\n            return {\\n                \"error\": f\"File not found: {file_path}\",\\n                \"success\": False\\n            }\\n        except Exception as e:\\n            return {\\n                \"error\": f\"Error reading file: {str(e)}\",\\n                \"success\": False\\n            }\\n    \\n    def find(self, pattern: str = \"*\", directory: str = \".\") -> Dict[str, Any]:\\n        \"\"\"\\n        Find files matching a pattern (similar to Unix find command)\\n        \\n        Args:\\n            pattern: File name pattern (supports wildcards, default: \"*\" for all files)\\n            directory: Directory to search in (relative to root_dir)\\n            \\n        Returns:\\n            Dictionary with list of matching files\\n        \"\"\"\\n        try:\\n            # Handle directory path properly\\n            if directory == \".\":\\n                search_dir = self.root_dir\\n            else:\\n                # Remove leading/trailing slashes for consistency\\n                directory = directory.strip(\\'/\\')\\n                search_dir = os.path.join(self.root_dir, directory)\\n            \\n            # Security check\\n            real_path = os.path.realpath(search_dir)\\n            if not real_path.startswith(self.root_dir):\\n                return {\\n                    \"error\": f\"Access denied: Path outside root directory\",\\n                    \"success\": False\\n                }\\n            \\n            # Check if directory exists\\n            if not os.path.exists(real_path):\\n                return {\\n                    \"error\": f\"Directory not found: {directory}\",\\n                    \"success\": False\\n                }\\n            \\n            # Use glob to find matching files\\n            matches = []\\n            for root, dirs, files in os.walk(real_path):\\n                # Filter hidden directories and __pycache__\\n                dirs[:] = [d for d in dirs if not d.startswith(\\'.\\') and d != \\'__pycache__\\']\\n                \\n                for file in files:\\n                    # Skip hidden files and .pyc files\\n                    if file.startswith(\\'.\\') or file.endswith(\\'.pyc\\'):\\n                        continue\\n                        \\n                    if glob_module.fnmatch.fnmatch(file, pattern):\\n                        # Get path relative to root_dir (not search_dir)\\n                        full_path = os.path.join(root, file)\\n                        rel_path = os.path.relpath(full_path, self.root_dir)\\n                        matches.append(rel_path)\\n            \\n            # Sort for consistency\\n            matches.sort()\\n            \\n            # Limit results for demonstration\\n            if len(matches) > 100:\\n                matches = matches[:100]\\n                truncated = True\\n            else:\\n                truncated = False\\n            \\n            return {\\n                \"pattern\": pattern,\\n                \"directory\": directory,\\n                \"matches\": matches,\\n                \"count\": len(matches),\\n                \"truncated\": truncated,\\n                \"success\": True\\n            }\\n        except Exception as e:\\n            return {\\n                \"error\": f\"Error finding files: {str(e)}\",\\n                \"success\": False\\n            }\\n    \\n    def grep(self, pattern: str, file_path: str = None, directory: str = None) -> Dict[str, Any]:\\n        \"\"\"\\n        Search for pattern in files (similar to Unix grep command)\\n        \\n        Args:\\n            pattern: Regular expression pattern to search for\\n            file_path: Single file to search in (optional)\\n            directory: Directory to search in (optional)\\n            \\n        Returns:\\n            Dictionary with matching lines\\n        \"\"\"\\n        try:\\n            matches = []\\n            files_searched = []\\n            \\n            if file_path:\\n                # Search in single file\\n                full_path = os.path.join(self.root_dir, file_path)\\n                real_path = os.path.realpath(full_path)\\n                \\n                if not real_path.startswith(self.root_dir):\\n                    return {\\n                        \"error\": f\"Access denied: Path outside root directory\",\\n                        \"success\": False\\n                    }\\n                \\n ', 'total_lines': 869, 'lines_read': 869, 'offset': 0, 'end_line': 869, 'truncated': True, 'success': True}, error=None, timestamp=1784337614.781894)"
  ],
  "metrics": {
    "ttft": 2.2344162464141846,
    "ttft_per_iteration": [
      2.2344162464141846,
      1.985018014907837,
      3.9584708213806152,
      6.417687892913818
    ],
    "total_time": 14.704215049743652,
    "iterations": 5,
    "tool_calls": 5,
    "cache_hits": 4,
    "cache_misses": 0,
    "prompt_tokens": 2224,
    "completion_tokens": 241,
    "cached_tokens": 1510
  },
  "mode": "sliding_window",
  "model": "kimi-k2.6",
  "task": "Find all Python files in this directory; read main.py and agent.py; summarize in 3 sentences."
}