""" Claude Code Backend ā€” Agentic coding backend powered by NVIDIA NIM models. Exposes an OpenAI-compatible /v1/chat/completions endpoint with built-in tools for file operations and bash execution. Architecture: Space 1 (better-chatbot) --> this backend --> NVIDIA NIM API The agentic loop: 1. Receive user message from Space 1 2. Send to NIM model with tool definitions 3. If model returns tool_calls, execute them and loop 4. If model returns text, stream it back to Space 1 5. Persist conversation in Postgres """ import os import shutil import threading import json import uuid import subprocess import asyncio import time import re import collections from pathlib import Path from typing import AsyncIterator, Optional, List, Dict, Any from pydantic import BaseModel import contextvars import urllib.parse workspace_var = contextvars.ContextVar("workspace_dir", default="/tmp/workspace") # --- Ultimate Agent Brain Imports --- from second_brain import SecondBrainWrapper from survival_watchdog import SurvivalWatchdog, get_metrics from swarm_llm import swarm from helix_state import helix_db from context_engine import ContextEngine # Instantiate singletons for the orchestrator SPACE_NAME = os.environ.get("SPACE_NAME", "space2-cerebrum") brain = SecondBrainWrapper(space_name=SPACE_NAME) context_engine = ContextEngine(brain) watchdog = SurvivalWatchdog() from fastapi import FastAPI, Request, Header, HTTPException from fastapi.responses import StreamingResponse, JSONResponse, HTMLResponse from fastapi.middleware.cors import CORSMiddleware from openai import AsyncOpenAI import anyio import asyncpg # --------------------------------------------------------------------------- # Globals & Activity Logs # --------------------------------------------------------------------------- activity_logs = collections.deque(maxlen=100) MODEL_STATUSES = {} ACTIVE_SESSIONS = set() def log_activity(msg: str): timestamp = time.strftime("%H:%M:%S") log_line = f"[{timestamp}] {msg}" activity_logs.append(log_line) print(log_line) # --------------------------------------------------------------------------- # Configuration # --------------------------------------------------------------------------- NIM_API_KEY = os.environ.get("NVIDIA_NIM_API_KEY", "") BACKEND_API_KEY = os.environ.get("BACKEND_API_KEY", "") DATABASE_URL = os.environ.get("DATABASE_URL", "") WORKSPACE_DIR = os.environ.get("WORKSPACE_DIR", "/tmp/workspace") BACKUP_GIT_REPO = os.environ.get("BACKUP_GIT_REPO", "") MAX_TOOL_ROUNDS = int(os.environ.get("MAX_TOOL_ROUNDS", "10")) # NIM models that reliably support tool/function calling TOOL_CAPABLE_MODELS = { "nvidia/nemotron-3-ultra-550b-a55b": "Nemotron 3 Ultra 550B (Agentic)", "z-ai/glm-5.1": "GLM 5.1 (Agentic)", "moonshotai/kimi-k2.6": "Kimi K2.6 (Agentic)", "minimaxai/minimax-m3": "MiniMax M3 (Agentic)", "stepfun-ai/step-3.7-flash": "Step 3.7 Flash (Agentic)", "minimaxai/minimax-m2.7": "MiniMax M2.7 (Agentic)", "meta/llama-3.1-70b-instruct": "Llama 3.1 70B (Agentic)", "meta/llama-3.1-405b-instruct": "Llama 3.1 405B (Agentic)", "qwen/qwen2.5-coder-32b-instruct": "Qwen 2.5 Coder 32B (Agentic)", "nvidia/llama-3.1-nemotron-70b-instruct": "Nemotron 70B (Agentic)", "meta/llama-3.3-70b-instruct": "Llama 3.3 70B (Agentic)", } # All models (tool-capable get agentic mode, others get plain chat) ALL_MODELS = { **TOOL_CAPABLE_MODELS, "deepseek-ai/deepseek-r1": "DeepSeek R1 (Chat only)", "mistralai/mistral-large-2-instruct": "Mistral Large 2 (Chat only)", } RECOMMENDED_MODEL = "nvidia/llama-3.1-nemotron-70b-instruct" # Ensure workspace exists Path(WORKSPACE_DIR).mkdir(parents=True, exist_ok=True) # --------------------------------------------------------------------------- # NIM Client # --------------------------------------------------------------------------- nim_client = AsyncOpenAI( base_url="https://integrate.api.nvidia.com/v1", api_key=NIM_API_KEY, ) # --------------------------------------------------------------------------- # Rate Limiting & Multi-Provider Setup # --------------------------------------------------------------------------- MISTRAL_API_KEY = os.environ.get("MISTRAL_API_KEY", "YyZD5l1SCwL83hJIKsRVF5I7Oklcxu4k") mistral_client = AsyncOpenAI( base_url="https://api.mistral.ai/v1", api_key=MISTRAL_API_KEY if MISTRAL_API_KEY else "dummy_key", ) if MISTRAL_API_KEY else None class MultiProviderRateLimiter: def __init__(self): self.nim_limit = 40 self.nim_window = 60 self.nim_calls = [] self.mistral_last_call = 0.0 self.lock = asyncio.Lock() async def wait_for_mistral(self): async with self.lock: now = time.time() elapsed = now - self.mistral_last_call if elapsed < 1.0: await asyncio.sleep(1.0 - elapsed) self.mistral_last_call = time.time() async def wait_for_nim(self): async with self.lock: now = time.time() self.nim_calls = [t for t in self.nim_calls if now - t < self.nim_window] if len(self.nim_calls) >= self.nim_limit - 2: sleep_time = self.nim_window - (now - self.nim_calls[0]) print(f"[RateLimiter] Approaching NIM rate limit (40 RPM). Sleeping {sleep_time:.2f}s...") await asyncio.sleep(sleep_time) self.nim_calls.append(time.time()) rate_limiter = MultiProviderRateLimiter() # --------------------------------------------------------------------------- # Tool Definitions (OpenAI function calling format) # --------------------------------------------------------------------------- TOOLS = [ { "type": "function", "function": { "name": "read_file", "description": "Read the contents of a file. Use this to inspect existing code, configs, or any text file.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Relative path to the file from the workspace root" } }, "required": ["path"] } } }, { "type": "function", "function": { "name": "write_file", "description": "Write content to a file. Creates the file if it doesn't exist, overwrites if it does. Creates parent directories automatically.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Relative path to the file from the workspace root" }, "content": { "type": "string", "description": "The full content to write to the file" } }, "required": ["path", "content"] } } }, { "type": "function", "function": { "name": "run_bash", "description": "Execute a bash command in the workspace directory. Use for installing packages, running scripts, git operations, etc. Commands run with a 30 second timeout.", "parameters": { "type": "object", "properties": { "command": { "type": "string", "description": "The bash command to execute" } }, "required": ["command"] } } }, { "type": "function", "function": { "name": "list_directory", "description": "List files and directories in a given path. Shows file sizes and directory markers.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Relative path to the directory from workspace root. Use '.' for the workspace root." } }, "required": ["path"] } } }, { "type": "function", "function": { "name": "grep_search", "description": "Search for a pattern in files within the workspace. Returns matching lines with file paths and line numbers.", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "The search pattern (supports basic regex)" }, "path": { "type": "string", "description": "Directory or file to search in, relative to workspace root. Defaults to '.'", } }, "required": ["pattern"] } } }, { "type": "function", "function": { "name": "web_search", "description": "Search the web for up-to-date information, news, papers, or documentation.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The search query (be specific and detailed)" } }, "required": ["query"] } } }, { "type": "function", "function": { "name": "web_read", "description": "Read the clean markdown content of any webpage URL to get detailed context, articles, documentation, or code.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The absolute URL of the webpage to read" } }, "required": ["url"] } } }, ] # --------------------------------------------------------------------------- # Tool Execution # --------------------------------------------------------------------------- def _safe_path(rel_path: str) -> Path: """Resolve a relative path safely within the workspace.""" workspace = Path(workspace_var.get()).resolve() target = (workspace / rel_path).resolve() # Prevent path traversal if not str(target).startswith(str(workspace)): raise ValueError(f"Path traversal detected: {rel_path}") return target def repair_arguments(func_name: str, args: dict) -> tuple[dict, list[str]]: notes = [] repaired_args = dict(args) # 1. Nesting extraction (e.g. {"path": {"path": "file.txt"}}) for key in list(repaired_args.keys()): val = repaired_args[key] if isinstance(val, dict) and key in val: repaired_args[key] = val[key] notes.append(f"Flattened nested parameter '{key}'") # 2. Markdown stripping from bash command if func_name == "run_bash" and "command" in repaired_args: cmd = repaired_args["command"] if isinstance(cmd, str): pattern = r"```(?:bash)?\s*(.*?)\s*```" match = re.search(pattern, cmd, re.DOTALL) if match: repaired_args["command"] = match.group(1).strip() notes.append("Stripped markdown code blocks from bash command") # 3. Stringified array conversion for key, val in repaired_args.items(): if isinstance(val, str) and val.strip().startswith("[") and val.strip().endswith("]"): try: parsed_arr = json.loads(val) if isinstance(parsed_arr, list): repaired_args[key] = parsed_arr notes.append(f"Converted stringified array for parameter '{key}' to native array") except: pass # 4. Optional empty objects replacing Null for key in list(repaired_args.keys()): if repaired_args[key] == {}: repaired_args[key] = None notes.append(f"Replaced empty object for parameter '{key}' with null") return repaired_args, notes # --------------------------------------------------------------------------- # Search and MCP Helpers # --------------------------------------------------------------------------- def sanitize_function_name(name: str) -> str: sanitized = re.sub(r'[^a-zA-Z0-9_\.\-]', '_', name) if not re.match(r'^[a-zA-Z_]', sanitized): sanitized = '_' + sanitized if len(sanitized) > 124: sanitized = sanitized[:124] return sanitized def create_mcp_tool_id(server_name: str, tool_name: str) -> str: san_server = sanitize_function_name(server_name) san_tool = sanitize_function_name(tool_name) max_len = 124 sep = "_" if len(san_server) + len(san_tool) + len(sep) > max_len: total = len(san_server) + len(san_tool) server_portion = int((len(san_server) / total) * (max_len - len(sep))) tool_portion = max_len - len(sep) - server_portion return f"{san_server[:server_portion]}{sep}{san_tool[:tool_portion]}" return f"{san_server}{sep}{san_tool}" async def get_active_mcp_tools(): """ Queries `mcp_server` table and returns: 1. List of OpenAI function definitions to append to dynamic tools list. 2. Dictionary mapping `mcp_tool_id` to `(server_name, original_tool_name, config)`. """ mcp_tools_list = [] mcp_mapping = {} if not db_pool: return mcp_tools_list, mcp_mapping try: async with db_pool.acquire() as conn: rows = await conn.fetch("SELECT name, config, tool_info FROM mcp_server WHERE enabled = true") for row in rows: server_name = row["name"] config_raw = row["config"] if isinstance(config_raw, str): config = json.loads(config_raw) else: config = config_raw tool_info_raw = row["tool_info"] if not tool_info_raw: continue if isinstance(tool_info_raw, str): tool_info = json.loads(tool_info_raw) else: tool_info = tool_info_raw for tool in tool_info: tool_name = tool.get("name") description = tool.get("description", "") input_schema = tool.get("inputSchema", {}) tool_id = create_mcp_tool_id(server_name, tool_name) mcp_mapping[tool_id] = (server_name, tool_name, config) mcp_tools_list.append({ "type": "function", "function": { "name": tool_id, "description": f"[from MCP server: {server_name}] {description}", "parameters": input_schema } }) except Exception as e: log_activity(f"[MCP Database Query Warning] Failed to load MCP tools: {e}") return mcp_tools_list, mcp_mapping async def execute_web_search(query: str) -> str: tavily_key = os.environ.get("TAVILY_API_KEY", "") if tavily_key: try: async with httpx.AsyncClient(timeout=15.0) as client: r = await client.post("https://api.tavily.com/search", json={ "api_key": tavily_key, "query": query, "search_depth": "basic", "max_results": 5 }) if r.status_code == 200: data = r.json() results = [] for item in data.get("results", []): results.append(f"Title: {item.get('title')}\nURL: {item.get('url')}\nContent: {item.get('content')}\n---") return "\n".join(results) if results else "No results found." except Exception as e: log_activity(f"[Tavily Error] {e}") exa_key = os.environ.get("EXA_API_KEY", "") if exa_key: try: async with httpx.AsyncClient(timeout=15.0) as client: r = await client.post("https://api.exa.ai/search", headers={ "x-api-key": exa_key, "Content-Type": "application/json" }, json={ "query": query, "numResults": 5, "text": True }) if r.status_code == 200: data = r.json() results = [] for item in data.get("results", []): results.append(f"Title: {item.get('title')}\nURL: {item.get('url')}\nContent: {item.get('text', '')[:2000]}\n---") return "\n".join(results) if results else "No results found." except Exception as e: log_activity(f"[Exa Error] {e}") try: escaped_query = urllib.parse.quote(query) async with httpx.AsyncClient(headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}, timeout=10.0) as client: r = await client.get(f"https://html.duckduckgo.com/html/?q={escaped_query}") if r.status_code == 200: try: from bs4 import BeautifulSoup soup = BeautifulSoup(r.text, 'html.parser') results = [] for a in soup.find_all('a', class_='result__snippet')[:5]: title_el = a.find_previous('a', class_='result__url') title = title_el.text.strip() if title_el else "No Title" url = title_el['href'] if title_el and 'href' in title_el.attrs else "" snippet = a.text.strip() results.append(f"Title: {title}\nURL: {url}\nSnippet: {snippet}\n---") return "\n".join(results) if results else "No results found." except Exception: snippets = re.findall(r']*>(.*?)', r.text, re.DOTALL) results = [] for s in snippets[:5]: clean_s = re.sub(r'<[^>]*>', '', s).strip() results.append(f"Snippet: {clean_s}\n---") return "\n".join(results) if results else "No results found." except Exception as e: log_activity(f"[DDG Scrape Error] {e}") return "Error: Web search failed. Setup API keys (TAVILY_API_KEY, EXA_API_KEY) for best results." async def execute_web_read(url: str) -> str: jina_url = f"https://r.jina.ai/{url}" try: async with httpx.AsyncClient(timeout=20.0) as client: r = await client.get(jina_url, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}) if r.status_code == 200: content = r.text if len(content) > 30000: content = content[:30000] + "\n\n[Truncated - webpage content is extremely long]" return content except Exception as e: log_activity(f"[Jina Reader Error] {e}") try: async with httpx.AsyncClient(timeout=15.0) as client: r = await client.get(url, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}) if r.status_code == 200: html = r.text clean = re.sub(r'.*?', '', html, flags=re.DOTALL | re.IGNORECASE) clean = re.sub(r'.*?', '', clean, flags=re.DOTALL | re.IGNORECASE) clean = re.sub(r'<.*?>', ' ', clean) clean = re.sub(r'\s+', ' ', clean).strip() if len(clean) > 15000: clean = clean[:15000] + "\n\n[Truncated]" return clean except Exception as e: log_activity(f"[Direct Scrape Error] {e}") return f"Error: Failed to read URL {url}." async def execute_tool(name: str, arguments: dict, mcp_mapping: dict = None) -> str: """Execute a tool and return its output as a string asynchronously.""" try: if name == "read_file": path = _safe_path(arguments["path"]) if not path.exists(): return f"Error: File not found: {arguments['path']}" if not path.is_file(): return f"Error: Not a file: {arguments['path']}" content = path.read_text(encoding="utf-8", errors="replace") if len(content) > 50000: return content[:50000] + f"\n\n[Truncated ā€” file is {len(content)} chars]" return content elif name == "write_file": path = _safe_path(arguments["path"]) path.parent.mkdir(parents=True, exist_ok=True) path.write_text(arguments["content"], encoding="utf-8") return f"Successfully wrote {len(arguments['content'])} chars to {arguments['path']}" elif name == "run_bash": command = arguments["command"] # Safety: block dangerous commands blocked = ["rm -rf /", "mkfs", "dd if=", ":(){", "fork bomb"] if any(b in command.lower() for b in blocked): return "Error: Command blocked for safety reasons" # ASYNC SUBPROCESS - This prevents the FastAPI server from freezing! process = await asyncio.create_subprocess_shell( command, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, cwd=workspace_var.get(), env={**os.environ, "HOME": "/tmp", "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin")}, ) try: stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=30) output = "" if stdout: output += stdout.decode('utf-8', errors='replace') if stderr: output += ("\n" if output else "") + f"[stderr] {stderr.decode('utf-8', errors='replace')}" if process.returncode != 0: output += f"\n[exit code: {process.returncode}]" if not output: output = "[command completed with no output]" except asyncio.TimeoutError: try: process.kill() except Exception: pass await process.communicate() return "Error: Command timed out after 30 seconds" if len(output) > 20000: output = output[:20000] + f"\n\n[Truncated ā€” output is {len(output)} chars]" return output elif name == "list_directory": path = _safe_path(arguments.get("path", ".")) if not path.exists(): return f"Error: Directory not found: {arguments.get('path', '.')}" if not path.is_dir(): return f"Error: Not a directory: {arguments.get('path', '.')}" entries = [] for item in sorted(path.iterdir()): if item.is_dir(): entries.append(f" šŸ“ {item.name}/") else: size = item.stat().st_size if size < 1024: size_str = f"{size}B" elif size < 1024 * 1024: size_str = f"{size/1024:.1f}KB" else: size_str = f"{size/(1024*1024):.1f}MB" entries.append(f" šŸ“„ {item.name} ({size_str})") return f"Contents of {arguments.get('path', '.')}:\n" + "\n".join(entries) if entries else "Empty directory" elif name == "grep_search": pattern = arguments["pattern"] search_path = arguments.get("path", ".") path = _safe_path(search_path) process = await asyncio.create_subprocess_exec( "grep", "-rn", "--include=*", pattern, str(path), stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, cwd=workspace_var.get(), ) try: stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=10) output = stdout.decode('utf-8', errors='replace') if stdout else "No matches found" except asyncio.TimeoutError: try: process.kill() except Exception: pass await process.communicate() return "Error: Grep search timed out" if len(output) > 10000: output = output[:10000] + "\n\n[Truncated]" return output elif name == "web_search": query = arguments.get("query") return await execute_web_search(query) elif name == "web_read": url = arguments.get("url") return await execute_web_read(url) elif mcp_mapping and name in mcp_mapping: server_name, original_tool_name, config = mcp_mapping[name] try: # Ensure registered on Space 4 await apost_json(f"{SPACE4_URL}/api/mcp/register", { "name": server_name, "config": config }) # Call tool on Space 4 call_res = await apost_json(f"{SPACE4_URL}/api/mcp/call", { "serverName": server_name, "toolName": original_tool_name, "arguments": arguments }) if isinstance(call_res, dict) and "error" in call_res: return f"Error from MCP server {server_name}: {call_res['error']}" if isinstance(call_res, dict) and "content" in call_res: texts = [] for item in call_res["content"]: if isinstance(item, dict) and item.get("type") == "text": texts.append(item.get("text", "")) return "\n".join(texts) return str(call_res) except Exception as e: return f"Error executing MCP tool {name}: {str(e)}" else: return f"Error: Unknown tool: {name}" except Exception as e: return f"Error executing {name}: {str(e)}" # --------------------------------------------------------------------------- # Database (Session Persistence) # --------------------------------------------------------------------------- db_pool: Optional[asyncpg.Pool] = None async def init_db(): """Initialize database connection pool and create tables.""" global db_pool if not DATABASE_URL: return try: db_pool = await asyncpg.create_pool( DATABASE_URL, ssl="require", min_size=1, max_size=3, max_inactive_connection_lifetime=300 ) async with db_pool.acquire() as conn: await conn.execute(""" CREATE TABLE IF NOT EXISTS agent_session_entries ( id BIGSERIAL PRIMARY KEY, project_key TEXT NOT NULL, session_id TEXT NOT NULL, subpath TEXT, entry JSONB NOT NULL, created_at TIMESTAMPTZ NOT NULL DEFAULT now() ); CREATE INDEX IF NOT EXISTS idx_session_key ON agent_session_entries (project_key, session_id, subpath, id); CREATE INDEX IF NOT EXISTS idx_project_session ON agent_session_entries (project_key, session_id); CREATE TABLE IF NOT EXISTS eternity_projects ( id SERIAL PRIMARY KEY, project_name VARCHAR(100) UNIQUE NOT NULL, goal TEXT NOT NULL, deadline TIMESTAMPTZ NOT NULL, current_mode VARCHAR(20) NOT NULL DEFAULT 'build', is_active BOOLEAN NOT NULL DEFAULT true, priority VARCHAR(20) NOT NULL DEFAULT 'low', roadmap JSONB DEFAULT '[]', latest_brief TEXT, created_at TIMESTAMPTZ NOT NULL DEFAULT now() ); """) except Exception as e: print(f"[DB] Warning: Could not initialize database: {e}") db_pool = None async def save_message(session_id: str, role: str, content: str = None, tool_calls: list = None, tool_call_id: str = None): """Save a message to the session store using the unified schema.""" if not db_pool: return msg = {"role": role} if content is not None: msg["content"] = content if tool_calls: msg["tool_calls"] = tool_calls if tool_call_id: msg["tool_call_id"] = tool_call_id try: async with db_pool.acquire() as conn: await conn.execute( "INSERT INTO agent_session_entries (project_key, session_id, subpath, entry) VALUES ($1, $2, $3, $4)", "fastapi-completions", session_id, None, json.dumps(msg) ) except Exception as e: print(f"[DB] Warning: Could not save message: {e}") async def load_session(session_id: str) -> list: """Load conversation history from the session store using the unified schema.""" if not db_pool: return [] try: async with db_pool.acquire() as conn: rows = await conn.fetch( "SELECT entry FROM agent_session_entries WHERE project_key = $1 AND session_id = $2 AND subpath IS NOT DISTINCT FROM $3 ORDER BY id", "fastapi-completions", session_id, None ) return [json.loads(row["entry"]) for row in rows] except Exception as e: print(f"[DB] Warning: Could not load session: {e}") return [] # --------------------------------------------------------------------------- # SSE Chunk Formatting (OpenAI delta format) # --------------------------------------------------------------------------- def make_chunk(request_id: str, model: str, content: str = "", finish_reason: str = None) -> str: """Create an OpenAI-compatible SSE chunk.""" delta = {} if content: delta["content"] = content if finish_reason and not content: delta = {} chunk = { "id": f"chatcmpl-{request_id}", "object": "chat.completion.chunk", "created": int(time.time()), "model": model, "choices": [{ "index": 0, "delta": delta, "finish_reason": finish_reason, }], } return f"data: {json.dumps(chunk)}\n\n" # --------------------------------------------------------------------------- # FastAPI Application # --------------------------------------------------------------------------- app = FastAPI(title="Claude Code Backend", version="1.0.0") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"], ) def auth(authorization: str = None): """Verify bearer token.""" if not BACKEND_API_KEY: return # No auth configured expected = f"Bearer {BACKEND_API_KEY}" if authorization != expected: raise HTTPException(status_code=401, detail="Unauthorized") async def check_models_health(): # Mark all models as statically ONLINE to save API RPM quotas for model in ALL_MODELS.keys(): MODEL_STATUSES[model] = {"status": "ONLINE (Stable)", "latency": "Fast", "raw_latency": 0.1} log_activity("[Health Check] Zero-cost status check complete: all models marked ONLINE (No API calls made).") @app.on_event("startup") async def startup(): await init_db() Path(WORKSPACE_DIR).mkdir(parents=True, exist_ok=True) # Initialize statuses for all models as ONLINE by default for model_id, display_name in ALL_MODELS.items(): MODEL_STATUSES[model_id] = {"status": "ONLINE", "latency": "0.10s", "raw_latency": 0.10} log_activity(f"FastAPI backend started. Workspace: {WORKSPACE_DIR}") # --------------------------------------------------------------------------- # /v1/chat/completions ā€” Main endpoint # --------------------------------------------------------------------------- AGENTIC_SYSTEM_PROMPT = """You are an expert coding assistant with access to tools for file operations and command execution. When the user asks you to create, edit, or debug code: 1. Use `list_directory` and `read_file` to understand the current state 2. Use `write_file` to create or modify files 3. Use `run_bash` to execute commands (install packages, run scripts, test code) 4. Use `grep_search` to find patterns in code IMPORTANT RULES: - Always use tools to take action. Do NOT just describe what to do ā€” actually DO it. - After writing code, run it to verify it works. - If a command fails, read the error and fix it. - Work in the /tmp/workspace directory. - Be concise in your explanations, but thorough in your tool usage. - You are running in a multi-round tool-execution loop. Do NOT output redundant greetings (e.g. "Hey saumil!", "Let me check...", "Ready to build...") in intermediate rounds. Only output them in your first response if appropriate. Be direct and proceed with tool calls. """ def compact_history(messages: list) -> list: """ Bulletproof conversation history compaction. Guarantees that the total context length stays well below the model's token limits. """ if not messages: return [] # Find and preserve the system prompt system_msg = None if messages[0].get("role") == "system": system_msg = messages[0] start_idx = 1 else: start_idx = 0 other_messages = messages[start_idx:] # 1. Truncate individually massive messages (e.g. file contents or massive bash outputs) # Even recent messages should be compacted if they are ridiculously large! compacted_others = [] for msg in other_messages: role = msg.get("role") content = msg.get("content") or "" msg_copy = dict(msg) if len(content) > 15000: msg_copy["content"] = content[:5000] + f"\n\n[... Truncated {len(content) - 10000} characters to prevent model context limits from overflowing ...]\n\n" + content[-5000:] compacted_others.append(msg_copy) # 2. Enforce total character size budget (max ~300,000 characters / ~75,000 tokens) # Iterate backwards from newest to oldest final_list = [] total_chars = 0 max_budget = 300000 for msg in reversed(compacted_others): msg_len = len(msg.get("content") or "") if total_chars + msg_len < max_budget or len(final_list) < 2: final_list.append(msg) total_chars += msg_len else: # Drop older messages once we exceed context window budget pass # Reverse back to chronological order final_list.reverse() if system_msg: final_list.insert(0, system_msg) if len(messages) != len(final_list): log_activity(f"[Auto-Compaction] Sliced context window from {len(messages)} down to {len(final_list)} messages (Total chars: {total_chars})") return final_list @app.post("/v1/chat/completions") async def chat_completions(request: Request, authorization: str = Header(None)): auth(authorization) body = await request.json() requested_model = body.get("model", "meta/llama-3.1-70b-instruct") messages = body.get("messages", []) stream = body.get("stream", False) session_id = body.get("session_id") or str(uuid.uuid4()) # Route Mistral queries natively to the Mistral API client = nim_client if "mistral" in requested_model.lower(): if mistral_client: client = mistral_client if requested_model == "mistralai/mistral-large-2-instruct": requested_model = "mistral-large-latest" is_agentic = requested_model in TOOL_CAPABLE_MODELS request_id = str(uuid.uuid4())[:8] ACTIVE_SESSIONS.add(session_id) log_activity(f"Session [{session_id[:6]}] connected. Model: {requested_model} | Provider: {'Mistral' if client == mistral_client else 'NIM'}") # Build message history final_messages = [] # Add agentic system prompt for tool-capable models if is_agentic: # Check if there's already a system message has_system = any(m.get("role") == "system" for m in messages) if has_system: # Prepend agentic prompt to existing system message for m in messages: if m["role"] == "system": final_messages.append({ "role": "system", "content": AGENTIC_SYSTEM_PROMPT + "\n\nAdditional instructions:\n" + m["content"] }) else: final_messages.append(m) else: final_messages.append({"role": "system", "content": AGENTIC_SYSTEM_PROMPT}) final_messages.extend(messages) else: final_messages = list(messages) # Perform auto-compaction before executing agent loops final_messages = compact_history(final_messages) # Save the user's message to DB user_msg = next((m for m in reversed(messages) if m.get("role") == "user"), None) if user_msg: await save_message(session_id, "user", user_msg.get("content", "")) session_folder = session_id[:8] if session_id else "default" active_workspace = os.path.join(WORKSPACE_DIR, session_folder) os.makedirs(active_workspace, exist_ok=True) if not stream: # Non-streaming: simple completion workspace_token = workspace_var.set(active_workspace) try: mcp_tools, mcp_mapping = await get_active_mcp_tools() active_tools = list(TOOLS) + mcp_tools kwargs = {"model": requested_model, "messages": final_messages} if is_agentic: kwargs["tools"] = active_tools kwargs["tool_choice"] = "auto" async with completions_semaphore: response = await client.chat.completions.create(**kwargs) content = response.choices[0].message.content or "" await save_message(session_id, "assistant", content) ACTIVE_SESSIONS.discard(session_id) log_activity(f"Session [{session_id[:6]}] finished (non-streaming)") return JSONResponse({ "id": f"chatcmpl-{request_id}", "object": "chat.completion", "created": int(time.time()), "model": requested_model, "choices": [{"index": 0, "message": {"role": "assistant", "content": content}, "finish_reason": "stop"}], }) except Exception as e: ACTIVE_SESSIONS.discard(session_id) return JSONResponse({"error": {"message": str(e), "type": "internal_error"}}, status_code=500) finally: workspace_var.reset(workspace_token) # Streaming + agentic loop async def generate() -> AsyncIterator[str]: nonlocal final_messages workspace_token = workspace_var.set(active_workspace) try: mcp_tools, mcp_mapping = await get_active_mcp_tools() active_tools = list(TOOLS) + mcp_tools async with completions_semaphore: for round_num in range(MAX_TOOL_ROUNDS + 1): # Perform auto-compaction before calling NIM API final_messages = compact_history(final_messages) kwargs = {"model": requested_model, "messages": final_messages, "stream": True} if is_agentic: kwargs["tools"] = active_tools kwargs["tool_choice"] = "auto" # Collect streamed response full_content = "" tool_calls_raw = {} # index -> {id, name, arguments_str} async for chunk in await client.chat.completions.create(**kwargs): choice = chunk.choices[0] if chunk.choices else None if not choice: continue delta = choice.delta # Stream text content to client if delta and delta.content: full_content += delta.content yield make_chunk(request_id, requested_model, delta.content) # Collect tool calls if delta and delta.tool_calls: for tc in delta.tool_calls: idx = tc.index if idx not in tool_calls_raw: tool_calls_raw[idx] = { "id": tc.id or f"call_{uuid.uuid4().hex[:8]}", "name": tc.function.name if tc.function and tc.function.name else "", "arguments": "" } if tc.function and tc.function.name: tool_calls_raw[idx]["name"] = tc.function.name if tc.id: tool_calls_raw[idx]["id"] = tc.id if tc.function and tc.function.arguments: tool_calls_raw[idx]["arguments"] += tc.function.arguments # Check for finish if choice.finish_reason == "stop": break if choice.finish_reason == "tool_calls": break # If no tool calls, we're done if not tool_calls_raw: await save_message(session_id, "assistant", full_content) yield make_chunk(request_id, requested_model, finish_reason="stop") yield "data: [DONE]\n\n" return # Execute tool calls tool_calls_list = [] for idx in sorted(tool_calls_raw.keys()): tc = tool_calls_raw[idx] tool_calls_list.append({ "id": tc["id"], "type": "function", "function": {"name": tc["name"], "arguments": tc["arguments"]} }) # Add assistant message with tool calls to history assistant_msg = {"role": "assistant", "content": full_content or None, "tool_calls": tool_calls_list} final_messages.append(assistant_msg) # Execute each tool and add results for tc in tool_calls_list: func_name = tc["function"]["name"] raw_args_str = tc["function"]["arguments"] try: func_args = json.loads(raw_args_str) except json.JSONDecodeError: # Attempt raw JSON repair repaired_str = raw_args_str.strip() if not repaired_str.startswith("{"): repaired_str = "{" + repaired_str if not repaired_str.endswith("}"): repaired_str = repaired_str + "}" try: func_args = json.loads(repaired_str) log_activity(f"Auto-fixed invalid JSON string for tool: {func_name}") except: func_args = {} # Perform semantic repairs repaired_args, repair_notes = repair_arguments(func_name, func_args) # Log activity log_activity(f"Tool execution: {func_name} args={repaired_args}") if repair_notes: for note in repair_notes: log_activity(f"[Tool Repair] {note}") # Show tool execution to user yield make_chunk(request_id, requested_model, f"\n\nšŸ”§ **{func_name}**") if repair_notes: yield make_chunk(request_id, requested_model, " *(Auto-Repaired)*") if func_name == "run_bash" and "command" in repaired_args: yield make_chunk(request_id, requested_model, f": `{repaired_args['command']}`\n") elif func_name == "read_file" and "path" in repaired_args: yield make_chunk(request_id, requested_model, f": `{repaired_args['path']}`\n") elif func_name == "write_file" and "path" in repaired_args: yield make_chunk(request_id, requested_model, f": `{repaired_args['path']}`\n") elif func_name == "list_directory": yield make_chunk(request_id, requested_model, f": `{repaired_args.get('path', '.')}`\n") elif func_name == "grep_search": yield make_chunk(request_id, requested_model, f": `{repaired_args.get('pattern', '')}`\n") elif func_name == "web_search" and "query" in repaired_args: yield make_chunk(request_id, requested_model, f": `{repaired_args['query']}`\n") elif func_name == "web_read" and "url" in repaired_args: yield make_chunk(request_id, requested_model, f": `{repaired_args['url']}`\n") elif mcp_mapping and func_name in mcp_mapping: yield make_chunk(request_id, requested_model, f": calling tool\n") else: yield make_chunk(request_id, requested_model, "\n") # Execute the tool result = await execute_tool(func_name, repaired_args, mcp_mapping) # Append teaching note if repaired if repair_notes: result += f"\n\n[SYSTEM REPAIR NOTE: The harness automatically fixed formatting issues: {', '.join(repair_notes)}. Please strictly follow the tool's JSON schema in subsequent calls without these wrapping/formatting errors.]" # Show truncated result to user preview = result[:500] + ("..." if len(result) > 500 else "") yield make_chunk(request_id, requested_model, f"```\n{preview}\n```\n") # Add tool result to message history final_messages.append({ "role": "tool", "tool_call_id": tc["id"], "content": result, }) await save_message(session_id, "tool", result, tool_call_id=tc["id"]) # Continue the agentic loop (model processes tool results) # If we hit max rounds, finish yield make_chunk(request_id, requested_model, "\n\nāš ļø Reached maximum tool call rounds.") yield make_chunk(request_id, requested_model, finish_reason="stop") yield "data: [DONE]\n\n" except Exception as e: error_msg = f"\n\nāŒ Error: {str(e)}" yield make_chunk(request_id, requested_model, error_msg) yield make_chunk(request_id, requested_model, finish_reason="stop") yield "data: [DONE]\n\n" finally: workspace_var.reset(workspace_token) return StreamingResponse( generate(), media_type="text/event-stream", headers={ "Cache-Control": "no-cache", "X-Accel-Buffering": "no", "Connection": "keep-alive", }, ) # --------------------------------------------------------------------------- # /v1/models ā€” Model listing # --------------------------------------------------------------------------- @app.get("/v1/models") async def list_models(authorization: str = Header(None)): auth(authorization) models = [] for model_id, display_name in ALL_MODELS.items(): models.append({ "id": model_id, "object": "model", "created": 1700000000, "owned_by": "nvidia-nim", "permission": [], "root": model_id, "parent": None, }) return {"object": "list", "data": models} # --------------------------------------------------------------------------- # /health ā€” Health check # --------------------------------------------------------------------------- @app.get("/api/workspace/tree") async def get_workspace_tree(): def build_tree(current_path: Path, relative_to: Path) -> dict: name = current_path.name try: rel_path = str(current_path.relative_to(relative_to)).replace("\\", "/") except ValueError: rel_path = "" if rel_path == ".": rel_path = "" if current_path.is_dir(): children = [] try: for child in sorted(current_path.iterdir(), key=lambda x: (not x.is_dir(), x.name)): if child.name in [".git", "node_modules", ".next", "__pycache__", ".agents", ".gemini"]: continue children.append(build_tree(child, relative_to)) except Exception: pass return { "name": name or "workspace", "path": rel_path, "type": "directory", "children": children } else: return { "name": name, "path": rel_path, "type": "file", "size": current_path.stat().st_size if current_path.exists() else 0 } try: active_project = "" if db_pool: try: async with db_pool.acquire() as conn: row = await conn.fetchrow("SELECT project_name FROM eternity_projects WHERE is_active = true ORDER BY id DESC LIMIT 1") if row: active_project = row["project_name"].replace(" ", "-").lower() except Exception: pass project_folder = active_project if active_project else "default" w_path = (Path(WORKSPACE_DIR) / project_folder).resolve() if not w_path.exists(): w_path.mkdir(parents=True, exist_ok=True) return build_tree(w_path, w_path) except Exception as e: return {"error": str(e)} @app.get("/api/workspace/file") async def get_workspace_file(path: str): try: active_project = "" if db_pool: try: async with db_pool.acquire() as conn: row = await conn.fetchrow("SELECT project_name FROM eternity_projects WHERE is_active = true ORDER BY id DESC LIMIT 1") if row: active_project = row["project_name"].replace(" ", "-").lower() except Exception: pass project_folder = active_project if active_project else "default" w_path = (Path(WORKSPACE_DIR) / project_folder).resolve() token = workspace_var.set(str(w_path)) try: safe_p = _safe_path(path) if not safe_p.exists() or not safe_p.is_file(): raise HTTPException(status_code=404, detail="File not found") content = safe_p.read_text(encoding="utf-8", errors="replace") return {"path": path, "content": content} finally: workspace_var.reset(token) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/api/workspace/latest-screenshot") async def get_latest_screenshot(): try: active_project = "" if db_pool: try: async with db_pool.acquire() as conn: row = await conn.fetchrow("SELECT project_name FROM eternity_projects WHERE is_active = true ORDER BY id DESC LIMIT 1") if row: active_project = row["project_name"].replace(" ", "-").lower() except Exception: pass project_folder = active_project if active_project else "default" w_path = (Path(WORKSPACE_DIR) / project_folder).resolve() png_files = [] for p in w_path.rglob("*.png"): if any(part.startswith(".") for part in p.parts): continue try: mtime = p.stat().st_mtime png_files.append((p, mtime)) except Exception: pass if not png_files: raise HTTPException(status_code=404, detail="No screenshot found") newest = max(png_files, key=lambda x: x[1])[0] from fastapi.responses import FileResponse return FileResponse(str(newest)) except HTTPException as he: raise he except Exception as e: raise HTTPException(status_code=500, detail=str(e)) # --------------------------------------------------------------------------- # Dashboard and Status API # --------------------------------------------------------------------------- DASHBOARD_HTML = """ Claude Code Agent Console
Claude Code Agent Console LIVE
Workspace: /tmp/workspace
Active Sessions: 0

Nvidia NIM Models & Health Status

Checked every 15 mins
""" # --------------------------------------------------------------------------- # Concurrency Settings # --------------------------------------------------------------------------- completions_semaphore = asyncio.Semaphore(2) # --------------------------------------------------------------------------- # SessionStore API (Claude Agent SDK / Claude Code Compatible) # --------------------------------------------------------------------------- class SessionAppendRequest(BaseModel): project_key: str session_id: str subpath: Optional[str] = None entries: List[Dict[str, Any]] @app.post("/api/sessions/append") async def append_session_entries(req: SessionAppendRequest, authorization: str = Header(None)): auth(authorization) if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: # Check for mirror_error and log/alert if present for e in req.entries: if e.get("type") == "system" and e.get("subtype") == "mirror_error": log_activity(f"[ALERT] Claude Agent SDK reported mirror_error: {e.get('message')}") rows = [(req.project_key, req.session_id, req.subpath, json.dumps(e)) for e in req.entries] async with db_pool.acquire() as conn: await conn.executemany( "INSERT INTO agent_session_entries (project_key, session_id, subpath, entry) VALUES ($1, $2, $3, $4)", rows ) return {"status": "success"} except Exception as e: log_activity(f"[SessionStore Error] Failed append: {e}") raise HTTPException(status_code=500, detail=str(e)) class SessionLoadRequest(BaseModel): project_key: str session_id: str subpath: Optional[str] = None @app.post("/api/sessions/load") async def load_session_entries(req: SessionLoadRequest, authorization: str = Header(None)): auth(authorization) if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: async with db_pool.acquire() as conn: rows = await conn.fetch( "SELECT entry FROM agent_session_entries WHERE project_key=$1 AND session_id=$2 AND subpath IS NOT DISTINCT FROM $3 ORDER BY id", req.project_key, req.session_id, req.subpath ) return {"entries": [json.loads(r["entry"]) for r in rows]} except Exception as e: log_activity(f"[SessionStore Error] Failed load: {e}") raise HTTPException(status_code=500, detail=str(e)) class SessionListRequest(BaseModel): project_key: str @app.post("/api/sessions/list") async def list_sessions(req: SessionListRequest, authorization: str = Header(None)): auth(authorization) if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: async with db_pool.acquire() as conn: rows = await conn.fetch( "SELECT DISTINCT session_id FROM agent_session_entries WHERE project_key=$1", req.project_key ) return {"sessions": [r["session_id"] for r in rows]} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) class SessionDeleteRequest(BaseModel): project_key: str session_id: str @app.post("/api/sessions/delete") async def delete_session(req: SessionDeleteRequest, authorization: str = Header(None)): auth(authorization) if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: async with db_pool.acquire() as conn: await conn.execute( "DELETE FROM agent_session_entries WHERE project_key=$1 AND session_id=$2", req.project_key, req.session_id ) return {"status": "success"} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) from datetime import datetime, timedelta, timezone class EternityInitRequest(BaseModel): project_name: Optional[str] = None goal: Optional[str] = None problem_statement: Optional[str] = None deadline_hours: float priority: Optional[str] = "low" @app.post("/api/eternity/init") async def init_eternity_system(req: EternityInitRequest): if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: goal_text = req.goal or req.problem_statement or "Build a calculator" # Generate slugified project name if not provided import re slug = req.project_name if not slug: slug = re.sub(r'[^a-z0-9]+', '-', goal_text.lower()).strip('-')[:30] or "project" deadline = datetime.now(timezone.utc) + timedelta(hours=req.deadline_hours) async with db_pool.acquire() as conn: await conn.execute(""" INSERT INTO eternity_projects (project_name, goal, deadline, current_mode, priority, is_active) VALUES ($1, $2, $3, 'build', $4, true) ON CONFLICT (project_name) DO UPDATE SET goal = EXCLUDED.goal, deadline = EXCLUDED.deadline, current_mode = 'build', priority = EXCLUDED.priority, is_active = true """, slug, goal_text, deadline, req.priority or "low") log_activity(f"[Eternity Loop] Project '{slug}' initialized | Goal: '{goal_text}' | Priority: {req.priority}") return {"status": "success", "project_name": slug, "deadline": deadline.isoformat()} except Exception as e: log_activity(f"[Eternity Loop Error] Init failed: {e}") raise HTTPException(status_code=500, detail=str(e)) class EternityToggleRequest(BaseModel): project_name: str is_active: bool @app.post("/api/eternity/toggle") async def toggle_project(req: EternityToggleRequest): if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: async with db_pool.acquire() as conn: await conn.execute("UPDATE eternity_projects SET is_active = $1 WHERE project_name = $2", req.is_active, req.project_name) log_activity(f"[Eternity Loop] Project '{req.project_name}' is_active set to {req.is_active}") return {"status": "success"} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) class EternityPriorityRequest(BaseModel): project_name: str priority: str @app.post("/api/eternity/set-priority") async def set_project_priority(req: EternityPriorityRequest): if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: if req.priority not in ["supreme", "low"]: raise HTTPException(status_code=400, detail="Invalid priority") async with db_pool.acquire() as conn: await conn.execute("UPDATE eternity_projects SET priority = $1 WHERE project_name = $2", req.priority, req.project_name) log_activity(f"[Eternity Loop] Project '{req.project_name}' priority set to {req.priority}") return {"status": "success"} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/api/eternity/list") async def list_eternity_projects(): if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: async with db_pool.acquire() as conn: rows = await conn.fetch("SELECT project_name, goal, deadline, current_mode, is_active, priority, latest_brief, created_at FROM eternity_projects ORDER BY created_at DESC") projects = [] now = datetime.now(timezone.utc) for r in rows: deadline = r["deadline"] remaining = max(0.0, (deadline - now).total_seconds()) # Format remaining time if remaining > 0: h = int(remaining // 3600) m = int((remaining % 3600) // 60) rem_str = f"{h}h {m}m" else: rem_str = "Expired" projects.append({ "project_name": r["project_name"], "goal": r["goal"], "deadline": deadline.isoformat(), "current_mode": r["current_mode"], "is_active": r["is_active"], "priority": r["priority"], "latest_brief": r["latest_brief"], "created_at": r["created_at"].isoformat(), "time_remaining_str": rem_str }) return {"projects": projects} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/api/eternity/status") async def get_eternity_status(): if not db_pool: raise HTTPException(status_code=500, detail="Database not connected") try: async with db_pool.acquire() as conn: row = await conn.fetchrow("SELECT project_name, goal, deadline, current_mode, roadmap, latest_brief FROM eternity_projects WHERE is_active = true ORDER BY id DESC LIMIT 1") if not row: return {"active": False} deadline = row["deadline"] now = datetime.now(timezone.utc) remaining = max(0.0, (deadline - now).total_seconds()) return { "active": True, "project_name": row["project_name"], "goal": row["goal"], "deadline": deadline.isoformat(), "current_mode": row["current_mode"], "roadmap": json.loads(row["roadmap"]) if isinstance(row["roadmap"], str) else row["roadmap"], "latest_brief": row["latest_brief"], "time_remaining_seconds": remaining } except Exception as e: raise HTTPException(status_code=500, detail=str(e)) class ForgeExecuteRequest(BaseModel): task_id: str action: str prompt: str context_rules: Optional[str] = "" project_state_md: Optional[str] = "" # Karpathy CLAUDE.md protocol ā€” shared project context @app.post("/api/forge/execute") async def forge_execute(req: ForgeExecuteRequest, authorization: str = Header(None)): auth(authorization) log_activity(f"[Forge] Received execution request for task {req.task_id}: '{req.prompt[:80]}'") project_folder = "default" if "_" in req.task_id: parts = req.task_id.split("_") if len(parts) >= 2: project_folder = parts[1].replace(" ", "-").lower() active_workspace = os.path.join(WORKSPACE_DIR, project_folder) os.makedirs(active_workspace, exist_ok=True) workspace_token = workspace_var.set(active_workspace) try: # Load active MCP tools mcp_tools, mcp_mapping = await get_active_mcp_tools() active_tools = list(TOOLS) + mcp_tools # Inject project_state.md into system context if provided (Karpathy protocol) system_content = AGENTIC_SYSTEM_PROMPT + f"\nContext rules: {req.context_rules}" if req.project_state_md: system_content += f"\n\n---\n## Project State Document (read this first)\n{req.project_state_md}" messages = [ {"role": "system", "content": system_content}, {"role": "user", "content": req.prompt} ] final_messages = list(messages) success = False summary = "" error = None for round_num in range(MAX_TOOL_ROUNDS + 1): await rate_limiter.wait_for_nim() response = None try: response = await nim_client.chat.completions.create( model=RECOMMENDED_MODEL, messages=final_messages, tools=active_tools, tool_choice="auto", timeout=8.0 ) except Exception as e: log_activity(f"[Forge Fallback] NIM completions failed: {e}. Retrying with Mistral Large...") try: if mistral_client: response = await mistral_client.chat.completions.create( model="mistral-large-latest", messages=final_messages, tools=active_tools, tool_choice="auto" ) else: raise Exception("Mistral client not initialized") except Exception as e2: log_activity(f"[Forge Fallback Error] Mistral completions failed too: {e2}") raise e2 msg = response.choices[0].message tool_calls_payload = None if msg.tool_calls: tool_calls_payload = [] for tc in msg.tool_calls: tc_dict = { "id": tc.id, "type": tc.type, "function": { "name": tc.function.name, "arguments": tc.function.arguments } } tool_calls_payload.append(tc_dict) final_messages.append({ "role": "assistant", "content": msg.content, "tool_calls": tool_calls_payload }) if not msg.tool_calls: success = True summary = msg.content or "Task completed." break for tc in msg.tool_calls: func_name = tc.function.name raw_args_str = tc.function.arguments try: func_args = json.loads(raw_args_str) except Exception: repaired_str = raw_args_str.strip() if not repaired_str.startswith("{"): repaired_str = "{" + repaired_str if not repaired_str.endswith("}"): repaired_str = repaired_str + "}" try: func_args = json.loads(repaired_str) except Exception: func_args = {} result = await execute_tool(func_name, func_args, mcp_mapping) final_messages.append({ "role": "tool", "tool_call_id": tc.id, "content": result }) else: error = "Max tool call rounds exceeded." if success: return { "status": "success", "summary": summary, "error": None } else: return { "status": "error", "error": error or "Task execution failed." } except Exception as e: log_activity(f"[Forge Error] Task execution failed: {e}") return { "status": "error", "error": str(e) } finally: workspace_var.reset(workspace_token) async def db_heartbeat_loop(): log_activity("Database Heartbeat task started") while True: try: if db_pool: async with db_pool.acquire() as conn: await conn.execute("SELECT 1") log_activity("[Heartbeat] Pinged Aiven PostgreSQL successfully") except Exception as e: log_activity(f"[Heartbeat Warning] Failed to ping database: {e}") await asyncio.sleep(240) # Every 4 minutes import httpx # --------------------------------------------------------------------------- # Inter-Space URLs ā€” The 6-Space Topology # Space 2 (self) = shyota/claude-code-backend ā€” Cerebrum (Orchestrator) # Space 3 = augment17/claude-code-backend ā€” Forge (Coder) # Space 4 = shyota/mcp-cloud-host ā€” Library (Research + MCP) # Space 5 (Vault) = Space 3 also handles vault push since agent-worker-loop # HF slot is not provisioned. SPACE5_URL = SPACE3_URL. # Space 6 = augment17/mcp-cloud-host ā€” Sandbox (UI Tester) # --------------------------------------------------------------------------- SPACE3_URL = os.environ.get("SPACE3_URL", "https://augment17-claude-code-backend.hf.space") SPACE4_URL = os.environ.get("SPACE4_URL", "https://shyota-mcp-cloud-host.hf.space") SPACE5_URL = os.environ.get("SPACE5_URL", "https://augment17-agent-worker-loop-v2.hf.space") # Dedicated Vault container SPACE6_URL = os.environ.get("SPACE6_URL", "https://augment17-mcp-cloud-host.hf.space") async def get_active_projects(): async with db_pool.acquire() as conn: return await conn.fetch("SELECT project_name, goal, deadline, current_mode, priority FROM eternity_projects WHERE is_active = true ORDER BY id DESC") async def update_db_mode(project_name: str, mode: str): async with db_pool.acquire() as conn: await conn.execute("UPDATE eternity_projects SET current_mode = $1 WHERE project_name = $2", mode, project_name) async def update_db_brief(project_name: str, brief: str): async with db_pool.acquire() as conn: await conn.execute("UPDATE eternity_projects SET latest_brief = $1 WHERE project_name = $2", brief, project_name) async def update_db_roadmap(project_name: str, roadmap: list): async with db_pool.acquire() as conn: await conn.execute("UPDATE eternity_projects SET roadmap = $1 WHERE project_name = $2", json.dumps(roadmap), project_name) # Async HTTP POST ā€” replaces blocking urllib.request so the event loop never freezes async def apost_json(url: str, payload: dict, timeout: float = 120.0) -> dict: headers = { "Authorization": f"Bearer {BACKEND_API_KEY}", "Content-Type": "application/json" } try: async with httpx.AsyncClient(timeout=timeout) as client: r = await client.post(url, json=payload, headers=headers) r.raise_for_status() return r.json() except Exception as e: log_activity(f"[HTTP Error] POST to {url} failed: {e}") return {"status": "error", "error": str(e)} # --------------------------------------------------------------------------- # Karpathy project_state.md ā€” Shared Context Document # Each space receives this instead of bare prompts so agents always know # what phase they are in, what was built before, and what the research says. # --------------------------------------------------------------------------- def build_project_state_md(project_name: str, goal: str, mode: str, task_prompt: str, brief: str = "", prior_summary: str = "") -> str: ts = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%MZ") return f"""# Project State: {project_name} _Generated by Space 2 (Cerebrum) at {ts}_ ## Current Objective **Mode:** {mode.upper()} **Goal:** {goal} ## Instructions for Space 3 (The Forge) {task_prompt} ## Research Brief (from Space 4 ā€” The Library) {brief if brief else '_No research brief yet._'} ## Prior Work Summary {prior_summary if prior_summary else '_First cycle._'} ## Context Rules - Write deterministic, testable code. - All files go in /tmp/workspace/. - Return a 3-line summary: status, file changed, test result. """ async def execute_build_cycle(project_name: str, goal: str): log_activity(f"[Build Mode] Initiating build cycle for project '{project_name}' (goal: '{goal}')") # Track project state in our in-process graph DB helix_db.upsert_project(project_name, goal, "build", "low") await rate_limiter.wait_for_nim() plan_prompt = ( f"We are building: '{goal}'. " "Write ONE JSON instruction for Space 3 (The Forge) to code the next milestone. " "Respond ONLY with JSON: {\"prompt\": \"task description\", \"context_rules\": \"rules\"}" ) raw = "" try: res = await nim_client.chat.completions.create( model=RECOMMENDED_MODEL, messages=[{"role": "user", "content": plan_prompt}], max_tokens=1024, timeout=8.0 ) raw = res.choices[0].message.content.strip() except Exception as e: log_activity(f"[Model Fallback] NIM failed ({e}). Retrying with Mistral Large...") try: if mistral_client: res = await mistral_client.chat.completions.create( model="mistral-large-latest", messages=[{"role": "user", "content": plan_prompt}], max_tokens=1024 ) raw = res.choices[0].message.content.strip() else: raise Exception("Mistral client not initialized") except Exception as e2: log_activity(f"[Model Fallback Error] Mistral failed too: {e2}") return try: if raw.startswith("```"): raw = raw.split("```")[1].lstrip("json").strip() task = json.loads(raw) task_prompt = task.get("prompt", "") context_rules = task.get("context_rules", "") if isinstance(context_rules, (dict, list)): context_rules = json.dumps(context_rules) except Exception as e: log_activity(f"[Build Mode Error] Plan JSON parsing failed: {e} | Raw: {raw}") return # Use the context-locked Bell Curve prompt builder (enforces max 2500 tokens) playbook_file = f"space3-forge/debugging/{project_name}_playbook.md" state_md = brain.build_apex_prompt( project_name=project_name, goal=goal, mode="build", task_instruction=task_prompt, research_topic=project_name, current_file="" ) log_activity(f"[Build Mode] Dispatching to Space 3 (Forge) ā€” project '{project_name}': '{task_prompt[:80]}'") forge_res = await apost_json(f"{SPACE3_URL}/api/forge/execute", { "task_id": f"build_{project_name}_{int(time.time())}", "action": "execute_code", "prompt": task_prompt, "context_rules": context_rules, "project_state_md": state_md }) if forge_res.get("status") == "success": summary = forge_res.get("summary", "") log_activity(f"[Build Mode] Space 3 (Forge) SUCCESS for '{project_name}': {summary}") log_activity(f"[Build Mode] Triggering Space 6 (Sandbox) UI verification for '{project_name}'...") test_res = await apost_json(f"{SPACE6_URL}/api/sandbox/test", { "test_cmd": "verify_ui", "url": SPACE3_URL, "project_name": project_name }, timeout=30.0) verdict = test_res.get("verdict", "UNKNOWN") reason = test_res.get("reason", "") log_activity(f"[Build Mode] Space 6 (Sandbox) Verdict: {verdict} ā€” {reason}") # --- ACE: Reflect & Curate --- # Runs the feedback loop to update playbook rules on failure, or record patterns on success ace_verdict = await context_engine.reflect_and_curate( project_name=project_name, task_prompt=task_prompt, result_summary=summary, verdict=verdict, reason=reason ) log_activity(f"[Build Mode] ACE Reflection Result: {ace_verdict}") # Update in-process graph DB cycle and state helix_db.record_cycle(project_name, summary, verdict) # Log event directly to the local Second Brain git repo brain.append( "space2-cerebrum/loop_log.md", f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] Build Success | Project: {project_name} | Summary: {summary[:80]} | Verdict: {verdict}", f"[Brain Log] Record build cycle for {project_name}" ) log_activity("[Build Mode] Triggering Space 5 (Vault) backup commit...") vault_res = await apost_json(f"{SPACE5_URL}/api/vault/push", { "project_name": project_name, "summary": summary }, timeout=30.0) if vault_res.get("status") == "error": log_activity("[Build Mode] Space 5 (Vault) failed. Retrying backup via Space 3 (Forge) fallback...") vault_res = await apost_json(f"{SPACE3_URL}/api/vault/push", { "project_name": project_name, "summary": summary }, timeout=30.0) log_activity(f"[Build Mode] Vault backup status: {vault_res.get('status', 'unknown')}") else: log_activity(f"[Build Mode Warning] Space 3 (Forge) failure for '{project_name}': {forge_res.get('error')}") async def execute_eternity_cycle(project_name: str, goal: str): log_activity(f"[Eternity Mode] R&D cycle starting for project '{project_name}' (goal: '{goal}')") # Track project state in our in-process graph DB helix_db.upsert_project(project_name, goal, "eternity", "low") # Step 1: Ask Space 4 (Library) for research log_activity(f"[Eternity Mode] Querying Space 4 (Library) for '{project_name}'...") research_res = await apost_json(f"{SPACE4_URL}/api/research", { "query": f"novel algorithms, optimizations, and next features for: {goal}" }) brief = research_res.get("brief", "No new features found.") await update_db_brief(project_name, brief) log_activity(f"[Eternity Mode] Space 4 (Library) brief received ({len(brief)} chars)") # Save research brief to Second Brain wiki on GitHub research_file = f"space4-library/research/{project_name.replace(' ', '-')}.md" brain.write( research_file, f"# Research Brief: {project_name}\n\n{brief}", f"[Brain Research] Save brief for {project_name}" ) # Step 2: Ask NIM to plan next feature based on research await rate_limiter.wait_for_nim() plan_prompt = ( f"Goal: '{goal}'.\nResearch Brief:\n{brief[:2000]}\n\n" "Based on this research, plan the SINGLE most impactful next feature to implement. " "Respond ONLY with JSON: {\"prompt\": \"task\", \"context_rules\": \"rules\"}" ) raw = "" try: res = await nim_client.chat.completions.create( model=RECOMMENDED_MODEL, messages=[{"role": "user", "content": plan_prompt}], max_tokens=1024, timeout=8.0 ) raw = res.choices[0].message.content.strip() except Exception as e: log_activity(f"[Model Fallback] NIM failed ({e}). Retrying with Mistral Large...") try: if mistral_client: res = await mistral_client.chat.completions.create( model="mistral-large-latest", messages=[{"role": "user", "content": plan_prompt}], max_tokens=1024 ) raw = res.choices[0].message.content.strip() else: raise Exception("Mistral client not initialized") except Exception as e2: log_activity(f"[Model Fallback Error] Mistral failed too: {e2}") return try: if raw.startswith("```"): raw = raw.split("```")[1].lstrip("json").strip() task = json.loads(raw) task_prompt = task.get("prompt", "") context_rules = task.get("context_rules", "") if isinstance(context_rules, (dict, list)): context_rules = json.dumps(context_rules) except Exception as e: log_activity(f"[Eternity Mode Error] Plan JSON parsing failed: {e} | Raw: {raw}") return # Step 3: Build Karpathy context-locked prompt (Apex) state_md = brain.build_apex_prompt( project_name=project_name, goal=goal, mode="eternity", task_instruction=task_prompt, research_topic=project_name ) # Step 4: Dispatch to Space 3 (Forge) log_activity(f"[Eternity Mode] Dispatching to Space 3 (Forge): '{task_prompt[:80]}'") forge_res = await apost_json(f"{SPACE3_URL}/api/forge/execute", { "task_id": f"eternity_{project_name}_{int(time.time())}", "action": "execute_code", "prompt": task_prompt, "context_rules": context_rules, "project_state_md": state_md }) if forge_res.get("status") == "success": summary = forge_res.get("summary", "") log_activity(f"[Eternity Mode] Space 3 (Forge) SUCCESS: {summary}") # Update graph DB cycle helix_db.record_cycle(project_name, summary, "PASS") # Log event directly to the local Second Brain git repo brain.append( "space2-cerebrum/loop_log.md", f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] Eternity Cycle Success | Project: {project_name} | Summary: {summary[:80]}", f"[Brain Log] Record eternity cycle for {project_name}" ) # Trigger vault backup after each successful eternity cycle vault_res = await apost_json(f"{SPACE5_URL}/api/vault/push", { "project_name": project_name, "summary": summary }, timeout=30.0) if vault_res.get("status") == "error": log_activity("[Eternity Mode] Space 5 (Vault) failed. Retrying backup via Space 3 (Forge) fallback...") vault_res = await apost_json(f"{SPACE3_URL}/api/vault/push", { "project_name": project_name, "summary": summary }, timeout=30.0) log_activity(f"[Eternity Mode] Vault backup status: {vault_res.get('status', 'unknown')}") else: log_activity(f"[Eternity Mode Warning] Space 3 (Forge) failure: {forge_res.get('error')}") last_run_times = {} # project_name -> last execution timestamp IDLE_TASK_INTERVAL = 1800 # Run idle tasks every 30 min when no active projects _last_idle_run = 0.0 async def execute_idle_worker_cycle(): """Idle Worker Protocol ā€” runs when no active projects exist. Dispatches self-improvement tasks to Space 3 (Forge) to keep all containers warm and utilise free compute.""" log_activity("[Idle Worker] No active projects. Dispatching background optimisation task to Space 3...") idle_tasks = [ "Scan /tmp/workspace for any Python or JS files. For each file found, " "check if a corresponding test file exists. If not, write one basic unit test and run it. " "Return a summary of files scanned and tests added.", "Scan /tmp/workspace for duplicate function definitions across files. " "Report any found and refactor the worst one to a shared utility module.", "Run a syntax check on all files in /tmp/workspace and fix any issues found.", ] import random chosen = random.choice(idle_tasks) state_md = build_project_state_md( project_name="idle-maintenance", goal="system self-improvement", mode="idle", task_prompt=chosen ) res = await apost_json(f"{SPACE3_URL}/api/forge/execute", { "task_id": f"idle_{int(time.time())}", "action": "execute_code", "prompt": chosen, "context_rules": "Be concise. Fix only what is necessary. Return a 3-line summary.", "project_state_md": state_md }, timeout=60.0) log_activity(f"[Idle Worker] Space 3 response: {res.get('status')} ā€” {str(res.get('summary', ''))[:120]}") async def run_eternity_loop(): global _last_idle_run log_activity("[Eternity Loop] Async orchestration task started on FastAPI main loop.") loop_counter = 0 while True: try: if not db_pool: await asyncio.sleep(10) continue projects = await get_active_projects() if not projects: # --- Idle Worker Protocol --- now_ts = time.time() if now_ts - _last_idle_run >= IDLE_TASK_INTERVAL: _last_idle_run = now_ts await execute_idle_worker_cycle() await asyncio.sleep(60) continue loop_counter += 1 for p in projects: name = p["project_name"] goal = p["goal"] deadline = p["deadline"] current_mode = p["current_mode"] priority = p["priority"] # Priority gating: "low" skips 3 of every 4 cycles to conserve RPM if priority == "low" and (loop_counter % 4) != 0: log_activity(f"[Eternity Loop] Skipping low-priority '{name}' this cycle ({loop_counter})") continue now = datetime.now(timezone.utc) if current_mode == "build" and now >= deadline: await update_db_mode(name, "eternity") current_mode = "eternity" log_activity(f"[Eternity Loop] Project '{name}' deadline passed ā€” transitioned to Eternity R&D Mode.") if current_mode == "build": await execute_build_cycle(name, goal) else: last_run = last_run_times.get(name, 0.0) now_ts = time.time() interval = int(os.environ.get("ETERNITY_LOOP_INTERVAL", "3600")) if now_ts - last_run < interval: remaining_min = int((interval - (now_ts - last_run)) / 60) log_activity(f"[Eternity Loop] Project '{name}' sleeping ā€” next R&D in {remaining_min}m") continue last_run_times[name] = now_ts await execute_eternity_cycle(name, goal) # Base check interval: 5 minutes await asyncio.sleep(300) except Exception as e: log_activity(f"[Eternity Loop Error] Unhandled exception: {e}") await asyncio.sleep(60) @app.get("/", response_class=HTMLResponse) async def dashboard(): return HTMLResponse(content=DASHBOARD_HTML) # --------------------------------------------------------------------------- # Vault Push Endpoint (Space 5 co-hosted here on Space 3) # Space 2 (Cerebrum) calls this after every successful build/eternity cycle. # It zips /tmp/workspace and logs the backup event. # A real git push would require SSH credentials injected via HF secrets. # --------------------------------------------------------------------------- class VaultPushRequest(BaseModel): project_name: Optional[str] = "unknown" summary: Optional[str] = "" @app.post("/api/vault/push") async def vault_push(req: VaultPushRequest, authorization: str = Header(None)): auth(authorization) workspace = "/tmp/workspace" archive = "/tmp/vault_snapshot.zip" try: if os.path.exists(workspace) and os.listdir(workspace): proc = await asyncio.create_subprocess_exec( "zip", "-r", archive, workspace, stdout=asyncio.subprocess.DEVNULL, stderr=asyncio.subprocess.DEVNULL ) await asyncio.wait_for(proc.communicate(), timeout=30) size_kb = int(os.path.getsize(archive) / 1024) if os.path.exists(archive) else 0 log_activity(f"[Vault] Snapshot created for '{req.project_name}': {size_kb}KB | {req.summary[:80]}") return {"status": "success", "archive_kb": size_kb, "project_name": req.project_name} else: log_activity(f"[Vault] Workspace empty ā€” no snapshot for '{req.project_name}'") return {"status": "skipped", "reason": "workspace empty"} except Exception as e: log_activity(f"[Vault Error] Snapshot failed for '{req.project_name}': {e}") return {"status": "error", "error": str(e)} @app.get("/api/logs") async def get_logs(): return list(activity_logs) @app.get("/api/metrics") async def metrics(): """Exposes live CPU/RAM and HelixStateDB graph stats for space monitoring.""" vm = watchdog.get_metrics() if hasattr(watchdog, "get_metrics") else get_metrics() return { "status": "success", "timestamp": time.time(), "space_resources": vm, "graph_database": helix_db.dump() if hasattr(helix_db, "dump") else {} } @app.get("/api/models-status") async def get_models_status(): status_list = [] for model_id, display_name in ALL_MODELS.items(): status_info = MODEL_STATUSES.get(model_id, {"status": "ONLINE (Unchecked)", "latency": "N/A"}) is_rec = model_id == RECOMMENDED_MODEL is_agentic = model_id in TOOL_CAPABLE_MODELS status_list.append({ "id": model_id, "name": display_name, "status": status_info["status"], "latency": status_info["latency"], "is_recommended": is_rec, "type": "Agentic (Tools)" if is_agentic else "Chat Only", }) # Sort: Recommended first, then Agentic, then Chat status_list.sort(key=lambda m: (not m["is_recommended"], m["type"] != "Agentic (Tools)", m["name"])) return status_list import shutil import threading import signal from fastapi.responses import FileResponse @app.get("/api/backup/download") async def download_backup(authorization: str = Header(None)): auth(authorization) snapshot_dir = "/tmp/workspace_snapshot" archive_base = "/tmp/workspace_backup_download" archive_zip = archive_base + ".zip" # Clean up old files/folders for path in [snapshot_dir, archive_zip]: if os.path.exists(path): try: if os.path.isdir(path): shutil.rmtree(path) else: os.unlink(path) except Exception: pass try: # 1. Atomic-like snapshot copy (ignoring temporary files) shutil.copytree(WORKSPACE_DIR, snapshot_dir, symlinks=True, ignore=shutil.ignore_patterns('.git', 'node_modules', '.next')) # 2. Archive the snapshot folder to disk to prevent OOM memory spike shutil.make_archive(archive_base, 'zip', snapshot_dir) # 3. Clean up the snapshot directory immediately shutil.rmtree(snapshot_dir) if not os.path.exists(archive_zip): raise HTTPException(status_code=500, detail="Failed to create zip archive") return FileResponse(archive_zip, media_type="application/zip", filename="workspace_backup.zip") except Exception as e: if os.path.exists(snapshot_dir): shutil.rmtree(snapshot_dir) raise HTTPException(status_code=500, detail=str(e)) class SetModelRequest(BaseModel): model: str @app.post("/api/settings/model") async def set_recommended_model(req: SetModelRequest, authorization: str = Header(None)): auth(authorization) global RECOMMENDED_MODEL if req.model not in TOOL_CAPABLE_MODELS: raise HTTPException(status_code=400, detail="Invalid or unsupported agent model") RECOMMENDED_MODEL = req.model log_activity(f"[Settings] Recommended model manually updated to: {RECOMMENDED_MODEL}") return {"status": "ok", "recommended_model": RECOMMENDED_MODEL} @app.get("/health") async def health(): return { "status": "ok", "workspace": WORKSPACE_DIR, "workspace_exists": Path(WORKSPACE_DIR).exists(), "db_connected": db_pool is not None, "models_count": len(ALL_MODELS), "recommended_model": RECOMMENDED_MODEL, "active_sessions": len(ACTIVE_SESSIONS), } # --------------------------------------------------------------------------- # Watchdog Daemon for Claude Code Subprocesses (Orphan Reaper) # --------------------------------------------------------------------------- def run_watchdog(): log_activity("System Watchdog Daemon started (PPID-based Orphan detection)") while True: try: import psutil for proc in psutil.process_iter(['pid', 'ppid', 'name', 'cmdline', 'status']): try: cmd = " ".join(proc.info['cmdline'] or []) # Match the CLI binary (looks like claude-code or anthropic CLI wrapper) if "claude" in cmd.lower() or "anthropic" in cmd.lower(): ppid = proc.info['ppid'] pid = proc.info['pid'] # Is the parent still alive and not a zombie? parent_exists = False if ppid != 1: # Orphaned processes get reparented to PID 1 in Linux try: parent_proc = psutil.Process(ppid) if parent_proc.is_running() and parent_proc.status() != psutil.STATUS_ZOMBIE: parent_exists = True except psutil.NoSuchProcess: pass if not parent_exists: log_activity(f"[Watchdog SIGKILL] Reaping orphaned Claude Code process PID {pid} (PPID {ppid})") proc.terminate() time.sleep(2) if proc.is_running(): proc.kill() except Exception: continue except ImportError: # Fallback zero-dependency shell parser using /proc try: # Find all processes and examine their parent PID out = subprocess.check_output("ps -o pid,ppid,args | grep -E 'claude|anthropic' | grep -v grep", shell=True, text=True) for line in out.strip().split("\n"): parts = line.strip().split(None, 2) if len(parts) >= 2: pid = int(parts[0]) ppid = int(parts[1]) # Check if parent pid exists/is alive parent_exists = False if ppid != 1: # Check /proc/[ppid] directory if os.path.exists(f"/proc/{ppid}"): parent_exists = True if not parent_exists: log_activity(f"[Watchdog SIGKILL Fallback] Reaping orphaned process PID {pid} (PPID {ppid})") try: os.kill(pid, signal.SIGTERM) time.sleep(2) os.kill(pid, signal.SIGKILL) except Exception: pass except Exception: pass except Exception as e: log_activity(f"[Watchdog Error] {e}") time.sleep(60) async def db_heartbeat_loop(): log_activity("Database Heartbeat task started") while True: try: if db_pool: async with db_pool.acquire() as conn: await conn.execute("SELECT 1") log_activity("[Heartbeat] Pinged Aiven PostgreSQL successfully") except Exception as e: log_activity(f"[Heartbeat Warning] Failed to ping database: {e}") await asyncio.sleep(240) # Every 4 minutes def run_backup_loop(): if os.environ.get("SPACE_NAME", "space2-cerebrum") == "space2-cerebrum": log_activity("Local Git Backup Loop disabled on Cerebrum (Space 2)") return log_activity("Local Git Backup Loop started") while True: # Wait 5 minutes between runs time.sleep(300) if not BACKUP_GIT_REPO: continue try: log_activity("[Backup] Starting local workspace backup...") # 1. Clean local backup directory backup_local_dir = "/tmp/git_backup_repo" if os.path.exists(backup_local_dir): shutil.rmtree(backup_local_dir) os.makedirs(backup_local_dir, exist_ok=True) # 2. Point-in-time snapshot copy snapshot_dir = "/tmp/workspace_snapshot" if os.path.exists(snapshot_dir): shutil.rmtree(snapshot_dir) shutil.copytree(WORKSPACE_DIR, snapshot_dir, symlinks=True, ignore=shutil.ignore_patterns('.git', 'node_modules', '.next')) # 3. Zip snapshot archive_base = "/tmp/workspace_backup_download" archive_zip = archive_base + ".zip" if os.path.exists(archive_zip): os.unlink(archive_zip) shutil.make_archive(archive_base, 'zip', snapshot_dir) shutil.rmtree(snapshot_dir) # 4. Handle Split if needed target_dest = os.path.join(backup_local_dir, "workspace_backup.zip") zip_size = os.path.getsize(archive_zip) max_part_size = 50 * 1024 * 1024 # 50MB if zip_size > max_part_size: subprocess.run(f"split -b 50M {archive_zip} {target_dest}.part", shell=True) else: shutil.copy(archive_zip, target_dest) os.unlink(archive_zip) # 5. Git commit and force push subprocess.run("git init", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) subprocess.run("git config user.name 'Backup Agent'", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) subprocess.run("git config user.email 'backup@agent.internal'", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) subprocess.run(f"git remote add origin {BACKUP_GIT_REPO}", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) subprocess.run("git checkout -b main", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) subprocess.run("git add -A", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) subprocess.run('git commit -m "Auto-backup: ' + time.strftime("%Y-%m-%d %H:%M:%S") + '"', shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) result = subprocess.run("git push origin main --force", shell=True, cwd=backup_local_dir, stdout=subprocess.DEVNULL) if result.returncode == 0: log_activity("[Backup] Sync completed successfully (git history purged)") else: log_activity("[Backup Error] Git push failed") except Exception as e: log_activity(f"[Backup Error] {e}") async def db_cleanup_loop(): log_activity("Database Retention Cleanup task started") while True: try: if db_pool: async with db_pool.acquire() as conn: result = await conn.execute( "DELETE FROM agent_session_entries WHERE created_at < NOW() - INTERVAL '30 days'" ) log_activity(f"[Cleanup] Nightly retention sweep complete. Status: {result}") except Exception as e: log_activity(f"[Cleanup Warning] Failed to run retention cleanup: {e}") await asyncio.sleep(86400) # Every 24 hours async def wiki_compactor_loop(): log_activity("[Compactor] Wiki auto-compactor background task started.") while True: try: await context_engine.compact_wiki() except Exception as e: log_activity(f"[Compactor Error] {e}") await asyncio.sleep(86400) # Every 24 hours @app.on_event("startup") async def startup_event(): # Initialize the Ultimate Brain components try: log_activity("[Startup] Initialising Second Brain (git clone)...") brain.initialize() log_activity("[Startup] Second Brain cache ready.") log_activity("[Startup] Warming up local SwarmLLM (Qwen2.5-1.5B)...") asyncio.create_task(swarm.warm_up()) except Exception as e: log_activity(f"[Startup Error] Brain init failed: {e}") # Start the Resource Watchdog background task asyncio.create_task(watchdog.run()) # Start the Git-Sync background task asyncio.create_task(brain.background_sync()) # Start the Wiki Auto-Compaction task asyncio.create_task(wiki_compactor_loop()) # Start the local backup loop thread threading.Thread(target=run_backup_loop, daemon=True).start() # Start the eternity R&D loop as an async task on the main event loop asyncio.create_task(run_eternity_loop()) # Start the db keep-alive loop on FastAPI event loop asyncio.create_task(db_heartbeat_loop()) # Start the db nightly retention cleanup loop asyncio.create_task(db_cleanup_loop()) # --------------------------------------------------------------------------- # Entrypoint # --------------------------------------------------------------------------- if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)